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QinetiQ Limited in Farnborough is offering a Data Scientist Apprentice position for 2026. The role involves working with data across various types, applying machine learning techniques, and gaining practical experience in data handling. The ideal candidate will have a strong foundation in mathematics and programming, and will be involved in project teams to enhance their skills in coding and data analysis processes. Benefits include mentoring and access to training platforms.

Your work will change lives. Including your own. Recursion is a leading, clinical-stage TechBio company decoding biology to industrialize drug discovery. Central to its mission is the Recursion Operating System (OS), a platform built across diverse technologies that continuously expands one of the world's largest proprietary biological, chemical and patient-centric datasets. Recursion leverages sophisticated machine-learning algorithms to distill from its dataset a collection of trillions of searchable relationships across biology and chemistry unconstrained by human bias. By commanding massive experimental scale-up to millions of wet lab experiments weekly-and massive computational scale-owning and operating one of the most powerful supercomputers in the world-Recursion is uniting technology, biology, chemistry and patient-centric data to advance the future of medicine. In this role, you will: Build, scale, and operate in a validated data platform. You will be a member of the platform team responsible for building, operating, and tuning a data platform with petabyte scale data, and maintaining a GxP-compliant data platform for clinical data. These platforms allow our users to discover and query across the breadth of our data at Recursion, which includes a chemistry library of billions of compounds, cellular microscopy images taken in millions of different experimental contexts, and millions of assay results, all supporting Recursion's drug discovery. Engineer Data Integrity and Traceability. You will ensure data flows with integrity in the validated environment by building the automated audit trails and lineage required by 21 CFR Part 11 making our datasets in the clinical datalake queryable and relatable for regulatory questions. Additionally, you'll work with biologists, chemists, and data scientists to build relatability and query-ability into these datasets so they can be used in the future to answer the sorts of questions we haven't even thought of asking yet. Act as a mentor, coach, and sponsor. You will share your technical knowledge and experiences, delivering impact, learning, and growth across teams at Recursion. The Team You'll Join You will join the Data Lake team that built and maintains our Data Lake/house. The team is responsible for relational and object storage and has the motto: all data flows to the Data Lake. The team solves the problem of making our diverse data discoverable, queryable, and relatable across datasets while we continue to add new data modalities as we grow. This will require collaboration with many different groups including teams building out reports, dashboards, and applications, teams finding and generating the required data for machine learning problems, and teams building and iterating on new data processing pipelines. Nice to Haves: Experience in a regulated environment (Fintech, Medicine, Nuclear Industry, etc.), Experience with Search (Elasticsearch), Vector/Graph databases, or deep experience scaling within the GCP ecosystem is a significant plus. Working Location & Compensation This is an office-based, hybrid role in our London office. Employees are expected to work in the office at least 50% of the time. At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is £75,900 - £101,900. You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. Recursion is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected under applicable federal, state, local, or provincial human rights legislation. Accommodations are available on request for candidates taking part in all aspects of the selection process.

Why Socure? Socure is building the identity trust infrastructure for the digital economy - verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day. We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won't be your place. If you want to help build the future of identity with a team that holds a high bar for itself - keep reading. About the Role We are seeking a highly analytical and impact-driven Senior Data Scientist to join our Data Science Data team at Socure. In this role, you will work at the intersection of data, fraud risk, and identity verification, transforming raw, complex datasets into actionable insights that directly improve our products and decisioning systems. You will own high-impact projects end to end: designing scalable data pipelines, building and evaluating models, and leading analytical deep-dives that shape how we use data to detect fraud and validate identity. You will also leverage emerging approaches, including agentic AI and LLM-powered systems, to automate data analysis, accelerate insight generation, and scale how we evaluate identity data and detect fraud patterns. This is an advanced individual-contributor role (IC4 / Senior) that requires deep technical expertise, strong business judgment, and alignment with Socure's leadership competencies, including continuous learning, effective communication, accountability, team development, decision making, and managing change. What You'll Do Design, build, and maintain scalable data pipelines and workflows to support analytics, fraud detection, model development, and ongoing data monitoring (e.g., using Spark, Airflow, or similar distributed systems). Leverage and build agentic AI and LLM-powered systems to automate data exploration, anomaly detection, vendor evaluation, and investigative workflows, increasing the speed and depth of insight generation. Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images, in support of fraud detection and identity verification use cases. Own data quality and integrity for critical datasets, implementing monitoring, validation checks, and anomaly detection to ensure reliable input to models and downstream decision systems. Take ownership of project outcomes from scoping through delivery, managing data quality, technical trade-offs, and timelines; proactively escrow risks and work cross-functionally to resolve challenges. Evaluate and integrate third-party data vendors and external datasets, including designing experiments to assess data quality, coverage, lift, and long term value for Socure's models and products. Collaborate closely with Product, Engineering, and Risk teams to define data requirements, shape roadmap priorities, and deliver insights that guide strategic decisions for fraud and identity products. Conduct in depth research to explore new data sources and develop novel algorithms and features that advance the state of the art in fraud detection, identity resolution, and risk scoring. Lead the end to end ML/analytics lifecycle for assigned projects: problem definition, data exploration, feature engineering, modeling, evaluation, deployment handoff, and post deployment monitoring where applicable. Present findings, trade offs, and recommendations to technical and executive stakeholders with clarity and influence, adapting communication for audiences ranging from engineers to non technical business leaders. Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning aligned to Socure's leadership competencies. Stay current with advancements in AI, machine learning, and data infrastructure (including LLMs and agentic frameworks), and apply innovative techniques to real world fraud and identity problems. Model Socure's embedded leadership competencies in day to day work: continuous learning, effective communication, accountability, team development, decision making, and managing change. What You Bring Master's or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or a related quantitative field; or equivalent professional experience. 5+ years of experience in data science, machine learning, or closely related roles, ideally in a high growth tech or fintech environment. Experience in fraud prevention, risk modeling, or identity verification, including working with noisy, adversarial, or high risk data environments. Proven experience working with large, messy, real world datasets to generate insights and drive measurable business impact (not limited to pure model development). Experience working with diverse data modalities, such as tabular data, text/language, point clouds, and images, and selecting appropriate modeling approaches for each. Strong proficiency in Python and SQL, with hands on experience using major ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit learn) for model development and evaluation. Deep understanding of machine learning algorithms, model evaluation techniques (e.g., AUC, lift, calibration, stability), and data pipeline development for both batch and near real time use cases. Experience building and maintaining data pipelines and workflows in distributed or large scale environments (e.g., Spark, Airflow, Databricks, or similar technologies). Demonstrated ability to evaluate and work with third party data vendors or external datasets, including designing tests for data quality, coverage, stability, and incremental lift over existing signals. Experience with LLMs and agentic AI frameworks/infrastructure (e.g., LangChain, LangGraph, Ray) is strongly preferred; ability to design or extend agentic workflows for analytics and data quality use cases is a plus. Demonstrated ability to proactively deliver complex outcomes, lead technical workstreams, mentor others, and influence cross functional decisions without formal authority. Excellent written and verbal communication skills, with the ability to translate complex data problems and model behavior into actionable business insights for both technical and non technical audiences. Commitment to continuous learning, professional integrity, and high standards of business ethics, consistent with Socure's leadership expectations. Please note: we are unable to provide sponsorship now, or in the future. Applicants must be located in one of the following metros ( 45 miles) to be considered: New York, Miami, DC, Seattle, San Francisco. Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need an accommodation during any stage of the application or hiring process-including interview or onboarding support-please reach out to your Socure recruiting partner directly.

Socure is seeking a Senior Data Scientist in the United Kingdom to enhance identity verification and fraud risk management. This role involves building scalable data pipelines, leveraging advanced AI techniques, and collaborating cross-functionally. Candidates should have at least 5 years' experience in data science, expertise in Python, SQL, and familiarity with machine learning frameworks like PyTorch and TensorFlow. The company values continuous learning and diversity, providing an environment for impactful work.

About client Well-funded and fast-growing deep-tech company founded in 2019. We are the biggest Quantum Software company in the EU. They are also one of the 100 most promising companies in AI in the world (according to CB Insights, 2023) with 150+ employees and growing, fully multicultural and international. Requirements Systems Programming Expertise: 10+ years of software engineering experience with strong proficiency in Python. You must be comfortable building system agents, APIs, and CLI tools. Deep Kubernetes Knowledge: You understand K8s internals beyond simple deployment. Experience with Custom Resource Definitions (CRDs), Operators, and the Kubernetes API server architecture. GPU Ecosystem Experience: Hands on experience managing NVIDIA GPU clusters. Familiarity with NVIDIA drivers, CUDA toolkit, and the container runtime (NVIDIA Container Toolkit). Linux Internals: Deep understanding of the Linux kernel, cgroups, namespaces, and system performance tuning. Infrastructure as Code: Mastery of declarative infrastructure tools (Terraform, Ansible) but with a focus on provisioning physical hardware rather than just cloud VMs. Problem Solving: A proven track record of debugging complex distributed systems where the root cause could be code, network, or silicon. Preferred qualifications HPC Background: Experience working with traditional supercomputing schedulers (Slurm, PBS) or modern batch schedulers (Volcano, Kueue, Ray). Bare Metal Provisioning: Experience with tools like Cluster API (CAPI), Metal3, Tinkerbell, Canonical MaaS, or OpenStack Ironic. High Speed Networking: Knowledge of RDMA, InfiniBand, GPUDirect, and how to expose these technologies to containerized workloads. AI/ML Familiarity: Understanding of how distributed training works (e.g., PyTorch Distributed, Megatron LM, DeepSpeed) and the infrastructure requirements of Large Language Models (LLMs). Observability: Experience building monitoring for hardware health (DCGM) and distributed tracing for long running jobs. Location Applicants must have legal authorization to work in the country where the position is based. What you will be doing Building the Control Plane: Designing and developing the software layer (APIs, Controllers, Agents) that automates the lifecycle of bare metal AI infrastructure. Orchestrating High Scale Compute: Architecting scheduling solutions for large scale distributed training jobs across massive clusters of GPUs (NVIDIA H200/B200/B300), ensuring efficient bin packing and gang scheduling. Optimizing the Fabric: Tuning the software defined networking layer to support low latency interconnects (InfiniBand/RDMA/RoCEv2) essential for multi node training. Developing Kubernetes Extensions: Writing custom Kubernetes Operators and CRDs to abstract complex hardware realities (topology awareness, GPU partitioning) into usable interfaces for our Data Scientists. Hardware Level Debugging: Investigating and resolving deep systems issues, ranging from PCIe bus errors and NCCL communication timeouts to kernel panics on bare metal nodes. Defining Standards: Creating the "Golden Image" for AI workloads, managing drivers, firmware, and OS optimizations to squeeze maximum performance out of the hardware. Perks & Benefits Indefinite contract. Equal pay guaranteed. Variable performance bonus. Signing bonus. Relocation package (if applicable). Private health insurance. Eligibility for educational budget according to internal policy. Hybrid opportunity. Flexible working hours. Working in a high paced environment, working on cutting edge technologies. Career plan. Opportunity to learn and teach. Progressive Company. Happy people culture.

About the Role: We are seeking a motivated and technically skilled AI Machine Learning Engineer to join our growing team based in Central London. This is an exciting opportunity for someone passionate about artificial intelligence and data-driven solutions to contribute to innovative projects across various industries. Whether you're early in your career or looking to transition into AI, we offer full training and a supportive environment to help you grow. Flexible and remote working arrangements are available. Key Responsibilities: Design, develop, and deploy machine learning models to solve real-world problems Collaborate with data scientists, software engineers, and product teams to integrate AI capabilities into scalable applications Preprocess and analyze large datasets for use in training and evaluation Monitor, maintain, and improve model performance over time Stay current with the latest research and advancements in machine learning and AI technologies Essential Requirements: Familiarity with data preprocessing techniques and data pipelines Strong analytical and problem-solving skills A degree in Computer Science, Engineering, Mathematics, or a related field or equivalent practical experience Awareness of AI ethics, fairness, bias mitigation, and emerging regulations around responsible AI Desirable Skills: Experience with cloud platforms (AWS, GCP, Azure) Knowledge of MLOps practices and tools Familiarity with natural language processing or computer vision Strong foundation in Python and machine learning libraries Understanding of key ML algorithms (e.g., regression, classification, clustering, neural networks) What We Offer: A starting salary of £39,000 with regular performance-based reviews Full training and mentorship to support your development in AI and machine learning Clear career progression paths into senior engineering, research, or leadership roles Flexible working with the option to work remotely part time Access to industry standard tools, cloud platforms, and AI research resources A collaborative and inclusive team culture based in Central London How to Apply: If you're ready to take the next step in your AI career, we'd love to hear from you. Submit your CV and a short cover letter outlining your interest in the role and any relevant experience. Company: Tech Recruitment UK Ltd Level of experience (years): Senior (5+ years of experience)

We are looking for a highly skilled Data Analytics Engineer to support product analytics, product marketing, customer support, and operational teams. This role enables data-driven decision-making by developing scalable data models and delivering high-quality insights across the business. The position works cross-functionally with engineering, data science, and business stakeholders and plays a key role in ensuring the accessibility, quality, and reliability of data. Responsibilities Data Modeling & Engineering Build and maintain scalable data models using dbt and Snowflake Transform raw data into clean, reliable datasets for analysis Apply engineering best practices to analytics code to ensure scalability and maintainability Insight & Business Impact Translate data models into actionable insights for stakeholders across product, marketing, and operations Partner with business teams to define analytics requirements and measurable success outcomes Collaboration & Stakeholder Engagement Work collaboratively with data engineers, data scientists, and business stakeholders Support cross-functional teams by delivering high-quality data solutions Data Governance & Quality Maintain data documentation, definitions, and lineage Advocate for and uphold data quality and coding standards through code reviews Tools & Technology Utilise Snowflake, dbt, Looker, Git, and modern data tooling to deliver analytics solutions Core Competencies for Success Collaborates - Builds strong partnerships across technical and business teams to achieve shared outcomes Drives Results - Consistently delivers high-quality outputs in a fast-paced, data-driven environment Ensures Accountability - Takes ownership of data quality, accuracy, and delivery standards Tech Savvy - Adopts and applies modern data technologies to improve efficiency and scalability Manages Complexity - Analyses complex datasets and translates them into clear, actionable insights Credentials and Experience 3+ years of experience in a data analyst, analytics engineer, data engineer, or similar role 2+ years of experience working with product and/or customer data 3+ years of hands on experience writing and optimising SQL for data analysis and transformation Experience using Python, R, or similar tools for data analysis and manipulation 1-3 years of experience working with modern data warehouses and transformation tools (e.g., Snowflake, dbt) Experience transforming raw data into clean, production-ready datasets and maintaining technical documentation Experience working with large-scale datasets (e.g., high-volume event or behavioural data) Exposure to API integrations and data ingestion processes is desirable Demonstrated ability to work effectively in a remote or distributed team environment Legal right to work in the United Kingdom Other Details The role is hybrid based in Bath, UK. As the office is a listed building, please let us know if accommodations are needed due to the lack of a lift. Salary range: £35,000 - £39,400 annually. The position is eligible for an annual bonus. Discovery Education is an equal opportunity employer. Discovery Education is committed to being an employer of choice, not just a good place to work, but a great and inclusive place to work. To that end, we strive to recruit and maintain a workforce that meaningfully represents the communities we serve. Qualified applicants will receive consideration for employment without regard to their race, color, religion, national origin, sex, sexual orientation, gender, protected veteran status or disabled status or, genetic information.

Posted Wednesday 24 June 2026 at 04:00 Expires Tuesday 14 July 2026 at 03:59 We are looking for a highly skilled Data Analytics Engineer to support product analytics, product marketing, customer support, and operational teams. This role enables data-driven decision-making by developing scalable data models and delivering high-quality insights across the business. The position works cross-functionally with engineering, data science, and business stakeholders and plays a key role in ensuring the accessibility, quality, and reliability of data. The successful candidate will become a specialist in our modern data stack, including Snowflake, dbt, Looker, and Hightouch, and will contribute to building a robust analytics capability at scale. In This Role You Will Data Modeling & Engineering Build and maintain scalable data models using dbt and Snowflake Transform raw data into clean, reliable datasets for analysis Apply engineering best practices to analytics code to ensure scalability and maintainability Insight & Business Impact Translate data models into actionable insights for stakeholders across product, marketing, and operations Partner with business teams to define analytics requirements and measurable success outcomes Collaboration & Stakeholder Engagement Work collaboratively with data engineers, data scientists, and business stakeholders Support cross-functional teams by delivering high quality data solutions Data Governance & Quality Maintain data documentation, definitions, and lineage Advocate for and uphold data quality and coding standards through code reviews Tools & Technology Utilise Snowflake, dbt, Looker, Git, and modern data tooling to deliver analytics solutions Core Competencies for Success Collaborates - Builds strong partnerships across technical and business teams to achieve shared outcomes Drives Results - Consistently delivers high quality outputs in a fast paced, data driven environment Ensures Accountability - Takes ownership of data quality, accuracy, and delivery standards Tech Savvy - Adopts and applies modern data technologies to improve efficiency and scalability Manages Complexity - Analyses complex datasets and translates them into clear, actionable insights Credentials and Experience 3+ years of experience in a data analyst, analytics engineer, data engineer, or similar role 2+ years of experience working with product and/or customer data 3+ years of hands on experience writing and optimising SQL for data analysis and transformation Experience using Python, R, or similar tools for data analysis and manipulation 1-3 years of experience working with modern data warehouses and transformation tools (e.g., Snowflake, dbt) Experience transforming raw data into clean, production ready datasets and maintaining technical documentation Experience working with large scale datasets (e.g., high volume event or behavioural data) Exposure to API integrations and data ingestion processes is desirable Demonstrated ability to work effectively in a remote or distributed team environment Legal right to work in the United Kingdom This role is hybrid based in Bath, UK. As the office is a listed building, we do not have a lift so please make us aware so we can make necessary accommodations. The hiring range for this position is between £35,000 - £39,400 annually, however, base pay offered may vary depending on job related knowledge, skills, experience, and location. Additionally, this position is eligible for an Annual Bonus. Benefits 28 days leave plus UK bank holidays Annual Winter Holiday Break (typically the last week of December) Income protection to support you during long term illness 4x salary Life Assurance Medicash cash back plan Private Medical Insurance Vivup cash back and discounted plan for 1000s of high street stores Cycle to work scheme Paid eye test and money towards glasses Numerous Employee Assistance Programs A social committee that helps us team build and celebrate wins Continuing Education AND Tuition Reimbursement Programs for when you choose to study further in your field as you work Mentorship program and collaboration with veteran leaders Constant opportunities for cross functional training and skill building Uncapped career growth Unlimited LinkedIn Learning Leadership training for people mangers Discovery Education is an equal opportunity employer. Discovery Education is committed to being an employer of choice, not just a good place to work, but a great and inclusive place to work. To that end, we strive to recruit and maintain a workforce that meaningfully represents the communities we serve. Qualified applicants will receive consideration for employment without regard to their race, color, religion, national origin, sex, sexual orientation, gender, protected veteran status or disabled status or, genetic information.

About Sygnature Discovery At Sygnature Discovery, we create exceptional scientific outcomes. For our customers, who bring life changing medicines to patients; and for our people, who help make this possible. We're 700+ scientists from over 50 countries, working as one across Europe and North America. With no competing internal programs, we offer total confidentiality and commitment. Over the past 20 years, we've earned a track record of real impact: 60+ advanced candidates, 200+ patents filed, 200+ active projects. Over 90% of our customers choose to stay with us - from biotechs racing to clinic to pharma companies that need specialised expertise for targeted challenges. About the role We are looking for an experienced IT professional to join our IT Department here at Sygnature Discovery. As a 2nd Line IT Technician, you will play a vital part in delivering high quality IT support across the business, supporting all aspects of IT technology for end users and computers. You will act as an escalation point for 1st line support, helping to resolve more complex issues whilst providing guidance and mentoring to help develop the wider team's skills and capabilities. This is a full time onsite position based at our headquarters in Nottingham. Role Responsibilities Provide general end user and computer support across scientific and business areas. Conduct rigorous troubleshooting and diagnosis for system issues, implementing effective solutions to maintain operational continuity. Respond to security incidents and investigations as required with leadership from the Security team. Conduct vulnerability scanning and proactive remediation as required with leadership from the Security team. Lead the efficient and robust computer build processes for new hardware. Contribute to end user training via various formats including written guides, video resources and in person training. Create and manage external user accounts required for remote access such as Egnyte cloud file storage collaboration folders, including access controls, folder creation and management of file stores. Oversee and manage users, computers, and groups in Active Directory and Microsoft 365 to ensure secure and efficient access control. Skills & Qualifications Technical training with certification, or relevant experience is required. Experience managing and supporting users and computers in a business setting, Active Directory, Office 365 and a range of operating systems and applications. Good working knowledge of Microsoft and Linux operating systems. Good awareness of IT and data security requirements and best practices. Logical thinking with good prioritisation, analytical and critical thinking skills. CompTIA Core Skill qualifications are desirable. Microsoft Desktop Administrator Associate certification is desirable. Benefits 25 days annual leave (plus bank holidays) Private Medical Insurance Life Insurance Employee Assistance Programme Enhanced Family Friendly Policies Sygnature Group Pension Scheme 1 paid volunteer day per year

Job Profile Description Join Kainos and Shape the Future At Kainos, we're problem solvers, innovators, and collaborators - driven by a shared mission to create real impact. Whether we're transforming digital services for millions, delivering cutting edge Workday solutions, or pushing the boundaries of technology, we do it together. We believe in a people first culture, where your ideas are valued, your growth is supported, and your contributions truly make a difference. Here, you'll be part of a diverse, ambitious team that celebrates creativity and collaboration. Ready to make your mark? Join us and be part of something bigger. MAIN PURPOSE OF THE ROLE & RESPONSIBILITIES IN THE BUSINESS As a Lead Data Scientist at Kainos, you will architect, design, and deliver advanced AI solutions leveraging state of the art machine learning, generative and agentic AI technologies. You will drive the adoption of modern AI frameworks, AIOps best practices and scalable cloud native architectures. Your role will involve hands on technical leadership, collaborating with customers to translate business challenges into trustworthy AI solutions and ensuring responsible AI practices throughout. As a technical mentor, you will foster a culture of innovation, continuous learning, and engineering excellence. It is a fast paced environment, so it is important for you to make sound, reasoned decisions. You will do this whilst learning about new technologies and approaches, with talented colleagues that will help you to develop and grow. You will manage, coach, and develop a small number of staff, with a focus on managing employee performance and assisting in their career development. You will also provide direction and leadership for your team as you solve challenging problems together. Minimum (essential) Requirements A minimum of a 2.1 degree in Computer Science, AI, Data Science, Statistics or a similar quantitative field. Deep understanding and development of AI/ML models, including time series, supervised/unsupervised learning, reinforcement learning and LLMs. Experience with the latest AI engineering approaches such as prompt engineering, retrieval augmented generation (RAG), and agentic AI. Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews, etc). Expertise in data engineering for AI: handling large scale, unstructured, and multimodal data. Understanding of responsible AI principles, model interpretability, and ethical considerations. Strong interpersonal skills with the ability to lead client projects and establish requirements in non technical language. Experience managing, coaching, and developing junior members of a team and wider community. Must have active UK Government Security Clearance. Desirable Demonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open source AI projects or research publications. Familiarity with AI security, privacy, and compliance standards e.g. ISO42001. Embracing our differences At Kainos, we believe in the power of diversity, equity and inclusion. We are committed to building a team that is as diverse as the world we live in, where everyone is valued, respected, and given an equal chance to thrive. We actively seek out talented people from all backgrounds, regardless of age, race, ethnicity, gender, sexual orientation, religion, disability, or any other characteristic that makes them who they are. We also believe every candidate deserves a level playing field. Our friendly talent acquisition team is here to support you every step of the way, so if you require any accommodations or adjustments, we encourage you to reach out. We understand that everyone's journey is different, and by having a private conversation we can ensure that our recruitment process is tailored to your needs.

London (hybrid, 3 days in office) Data Science at Marshmallow Our Data Science team partners across the business to turn data into better decisions, smarter products, and simpler customer journeys. We work closely with Product, Engineering, and Operations to build and ship models and AI systems that are reliable in production and deliver measurable impact. Within Data Science, this role sits in Claims, supporting the function and the broader ambition to automate more of the claims journey. Claims is one of Marshmallow's most important customer touchpoints, and we're looking for a Senior Data Scientist who can provide technical expertise across traditional ML and Generative AI, bring system-level thinking to how we scale decisioning, and confidently challenge proposals to ensure we build robust, sustainable solutions. What you'll be doing Build and iterate on multimodal AI models that reduce claims cost and improve claims processing, including models that analyse emails, documents, and claim summaries for operational teams Develop machine learning models that support claims automation, including use cases such as negotiation strategies, litigation strategies, and total loss prediction Explore and evaluate new data sources that could improve model performance and decision-making, such as fraud signals, open banking, and telematics data Design and build agentic AI solutions to automate and streamline claims workflows Collaborate closely with Product, Data, and Engineering teams to test hypotheses, develop new features, and turn ideas into production ready solutions Work with the MLOps team to improve data science and AI model infrastructure, including deployment, monitoring, evaluation, and feedback loops Help define the right technical approach for problems, balancing speed, quality, and scalability while ensuring solutions are practical for the business Set a strong standard for experimentation, measurement, and model performance, helping the team understand impact, uncertainty, and trade offs clearly Who You Are You think in systems: you can connect the dots between data science, engineering, and product to shape scalable solutions that build on each other over time. You're confident in challenging assumptions and pushing for the right approach, using strong communication skills to influence stakeholders across seniority levels and disciplines with clear, pragmatic reasoning. You thrive in ambiguity and change, staying resilient and effective during transitions while bringing structure, clarity, and momentum to complex problem spaces. You're motivated by real world impact, partnering closely with cross functional teams to drive meaningful automation and better customer outcomes across the claims journey. What You'll Bring Strong commercial experience delivering end to end machine learning solutions, from problem framing and experimentation through to production deployment and ongoing monitoring Hands on experience building and shipping production AI or machine learning systems, including evaluation, quality considerations, and integration into operational workflows Experience working on applied problems involving structured and unstructured data, with an interest in multimodal modelling and AI systems A strong statistical and modelling foundation, with experience working on risk based decisioning or other complex, uncertain problem domains Proven ability to work cross functionally with Product, Engineering, Operations, and MLOps to deliver scalable solutions Strong communication and stakeholder management skills, with confidence in discussing trade offs and pushing back constructively when needed Perks of the job Bonus scheme designed to reward high performance Private medical insurance with Vitality, mental health support with Oliva Personal learning budget and 2 dedicated L&D days a year Monthly flexible benefits budget to spend as you choose 25 days holiday plus bank holidays 4 weeks Work From Anywhere per year We are able to offer visa sponsorship for this position. Our process Initial call with a member from our Talent Team (30 mins) Past Experience interview with Hiring Manager (60 mins) Technical interview with a couple of the team (90 mins) Culture interview (60 mins) Diversity of thought We know the best ideas come from having different perspectives in the room - and we're committed to hiring fairly, regardless of background, identity or experience. If you see yourself in this role, we'd encourage you to apply.

MAIN PURPOSE OF THE ROLE & RESPONSIBILITIES IN THE BUSINESS As a Lead Data Scientist at Kainos, you will architect, design, and deliver advanced AI solutions leveraging state of the art machine learning, generative and agentic AI technologies. You will drive the adoption of modern AI frameworks, AIOps best practices and scalable cloud native architectures. Your role will involve hands on technical leadership, collaborating with customers to translate business challenges into trustworthy AI solutions and ensuring responsible AI practices throughout. As a technical mentor, you will foster a culture of innovation, continuous learning, and engineering excellence. It is a fast paced environment, so it is important for you to make sound, reasoned decisions. You will do this whilst learning about new technologies and approaches, with talented colleagues that will help you to develop and grow. You will manage, coach, and develop a small number of staff, with a focus on managing employee performance and assisting in their career development. You will also provide direction and leadership for your team as you solve challenging problems together. Minimum (essential) Requirements A minimum of a 2.1 degree in Computer Science, AI, Data Science, Statistics or in a similar quantitative field. Have a deep understanding and developing of AI/ML models, including time series, supervised/unsupervised learning, reinforcement learning and LLMs. Experience with the latest AI engineering approaches such as prompt engineering, retrieval augmented generation (RAG), and agentic AI. Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews etc). Expertise in data engineering for AI: handling large scale, unstructured, and multimodal data. Understanding of responsible AI principles, model interpretability, and ethical considerations. Strong interpersonal skills with the ability to lead client projects and establish requirements in non technical language. We are passionate about developing people, you will bring experience in managing, coaching, and developing junior members of a team and wider community. Desirable Demonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open source AI projects or research publications. Familiarity with AI security, privacy, and compliance standards e.g. ISO42001. Embracing our differences At Kainos, we believe in the power of diversity, equity and inclusion. We are committed to building a team that is as diverse as the world we live in, where everyone is valued, respected, and given an equal chance to thrive. We actively seek out talented people from all backgrounds, regardless of age, race, ethnicity, gender, sexual orientation, religion, disability, or any other characteristic that makes them who they are. We also believe every candidate deserves a level playing field.

Dangote Industries Limited is seeking a Senior Data Scientist to join its Data Science team in London (hybrid). You will work closely with Product, Engineering, and Operations to build robust AI solutions that enhance decision-making in claims processing. The ideal candidate has extensive experience in machine learning, a strong statistical foundation, and excellent communication skills to influence stakeholders. The role offers a variety of perks, including private medical insurance and a bonus scheme.

Are you excited to apply advanced analytics and machine learning to real world aviation challenges? Would you like to shape best practices and mentor others while remaining hands on with high impact data science work? About the Role As a Principal Data Scientist, you will be a senior individual contributor who brings deep technical expertise while shaping best practices across the data science community. You will work closely with partners across product, engineering, and the business to design and deliver data driven solutions. This role combines technical leadership, collaboration, and hands on development to solve complex, real world problems in aviation analytics. Responsibilities Act as a technical authority for advanced analytics, machine learning, and generative AI, defining and evolving best practices for scalable and robust solutions Provide thought leadership on emerging technologies and methodologies relevant to aviation analytics Mentor and coach data scientists, supporting continuous learning and technical excellence Collaborate with product managers, engineers, and domain experts to align data science initiatives with business goals Translate complex technical insights into clear, actionable recommendations for stakeholders Lead by example through hands on coding, modelling, and solution design Design, prototype, and validate innovative approaches to high impact problems Partner with engineering teams to deploy models into production environments Requirements Strong experience working with large, complex, real world datasets, ideally in aviation or related industries Expertise in data wrangling, feature engineering, and building scalable data pipelines Deep expertise in at least one advanced analytics area, with experience across several of the following: predictive modelling on tabular data, deep learning, NLP and large language models, generative AI pipelines, simulations, graph based models, time series forecasting, geospatial modelling, causal inference, reinforcement learning, optimisation, or anomaly detection Proven experience deploying, or partnering to deploy, customer facing machine learning systems into production Ability to collaborate across disciplines and communicate technical concepts to non technical audiences Benefits We are delighted to offer country specific benefits. Click here to access benefits specific to your location. Equal Opportunity We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. USA Job Seekers: EEO Know Your Rights.

Depop is the community-powered circular fashion marketplace where anyone can buy, sell and discover desirable secondhand fashion. With a community of over 35 million users, Depop is on a mission to make fashion circular, redefining fashion consumption. Founded in 2011, the company is headquartered in London, with offices in New York and Manchester, and in 2021 became a wholly-owned subsidiary of Etsy.Find out more at Our mission is to make fashion circular and to create an inclusive environment where everyone is welcome, no matter who they are or where they're from. Just as our platform connects people globally, we believe our workplace should reflect the diversity of the communities we serve. We thrive on the power of different perspectives and experiences, knowing they drive innovation and bring us closer to our users. We're proud to be an equal opportunity employer, providing employment opportunities without regard to age, ethnicity, religion or belief, gender identity, sex, sexual orientation, disability, pregnancy or maternity, marriage and civil partnership, or any other protected status. We're continuously evolving our recruitment processes to ensure fairness and are open to accommodating any needs you might have. If, due to a disability, you need adjustments to complete the application, please let us know by sending an email with your name, the role to which you would like to apply, and the type of support you need to complete the application to . For any other non-disability related questions, please reach out to our Talent Partners. Role We're hiring a Senior Data Engineer to work on the analytics engineering platform at Depop. You'll work on developing Airflow pipelines, the deployment of dbt models, managing data ingestion through Fivetran and developing internal tooling. You'll also help enable data scientists and ML engineers to build and deploy models on reliable infrastructure. This role will be embedded in the Analytics Engineering team, which works across all data domains and supports data scientists, ML scientists and internal stakeholders on core datasets and key reporting. Responsibilities As a Senior Data Engineer, you can expect to: Build and maintain orchestration pipelines on Astronomer (Airflow), including automated model deployment for dbt users. Design and manage data ingestion and orchestration using Fivetran at scale Contribute to the team's vision and roadmap, and lead technically complex initiatives and be responsible for their success. Enhance our engineering efficiency by developing our internal tooling, CI/CD practices, and alerts/logging. Contribute to group initiatives or peer-level initiatives to improve the data platform. Foster growth and capabilities in our less experienced engineers through coaching, and support our data consumers in building their own models. Cultivate a collaborative data culture that empowers self-serve data and model creation. Requirements A track record of building and operating production data pipelines, including working with stakeholders to define requirements and SLAs. A strong experience using batch orchestration tools like Airflow and DBT. A strong of data warehousing patterns (e.g. lakehouse and medallion), performance optimisation (Spark/Delta), and data quality monitoring. Technical authoring and architecture design experience. Lead initiatives to build and deploy data products to support data scientists, engineers and agentic analysts. Experience with cloud data warehousing (Databricks preferred), IaC and Astronomer/Airflow. Mentor and support junior engineers and partner with data scientists on data engineering projects. Additional Information PMI and cash plan healthcare access with Bupa Subsidised counselling and coaching with Self Space Cycle to Work scheme with options from Evans or the Green Commute Initiative Employee Assistance Programme (EAP) for 24/7 confidential support Mental Health First Aiders across the business for support and signposting Work/Life Balance: 25 days annual leave with option to carry over up to 5 days 1 company-wide day off per quarter Impact hours: Up to 2 days additional paid leave per year for volunteering Fully paid 4 week sabbatical after completion of 5 years of consecutive service with Depop, to give you a chance to recharge or do something you love. Flexible Working: MyMode hybrid-working model with Flex, Office Based, and Remote options role dependant All offices are dog-friendly Ability to work abroad for 4 weeks per year in UK tax treaty countries Family Life: 18 weeks of paid parental leave for full-time regular employees IVF leave, shared parental leave, and paid emergency parent/carer leave Learn + Grow: Budgets for conferences, learning subscriptions, and more Mentorship and programmes to upskill employees Your Future: Life Insurance (financial compensation of 3x your salary) Pension matching up to 6% of qualifying earnings Employees enjoy free shipping on their Depop sales within the UK. Special milestones are celebrated with gifts and rewards!

The Consulting Solutions is hiring a Data Scientist, Safety, to analyze and solve complex problems related to harmful behaviors in AI systems. You will work with teams to measure product safety and build analytical systems. Your role includes responsibilities like detecting fraud and evaluating safety classifiers. Strong skills in SQL and Python are essential. This is an opportunity to have global impact while working on mission-critical safety challenges.

Machine Learning Platform Engineering We're on a mission to make money work for everyone, and our Machine Learning Platform team builds the systems that help teams across Monzo train, evaluate, deploy, and serve ML models and AI features safely and reliably. We work on backend services, Python libraries, model lifecycle tooling, evaluation workflows, and low latency serving systems. Our users are internal ML engineers, scientists, and product teams building with ML and LLMs. The work matters because machine learning powers many important decisions and experiences at Monzo, from fraud checks and credit decisions to customer operations. Location & Compensation London, UK (remote within the UK available). Salary £85,000 - £110,000 plus incentive awards tied to performance. Benefits include relocation support, visa sponsorship, flexible working hours, learning budget, and a full list of benefits. Responsibilities Develop backend services, platform APIs, and production systems using Go. Write Python libraries, workflows, and tooling used by our ML engineers and scientists. Implement feature platforms and data workflows with Chronon, Feast, and DataHub. Build model training pipelines and experiment tracking using Vertex AI and Comet. Maintain AI observability, evaluation, and tracing using Langfuse. Deploy and maintain real time serving on AWS and batch compute on GCP, including BigQuery data warehousing. Qualifications Strong backend engineering background with experience in Go and Python. Experience with ML or AI platforms, including pipelines, feature stores, model serving, experiment tracking, or LLM tooling. Designed and operated distributed systems that handle scale, concurrency, and failure. Focus on developer experience and removing friction for internal teams. Comfortable with ambiguity and ability to shape a platform as it grows. Experience with strongly typed languages and writing backend software. Curiosity about system behavior in production, including reliability, latency, quality, safety, and operational risk. This Might NOT Be the Right Fit If Your background is predominantly DevOps, SRE, or infrastructure operations. You are focused on data science or modelling rather than platform engineering. You have shipped AI product features but have not worked on the platform side (serving, evaluation, model lifecycle). Benefits Competitive salary £85,000 - £110,000 plus incentive awards. Relocation assistance to the UK and visa sponsorship. Flexible working hours and trust to work the hours that suit you. Annual learning budget of £1,000 for books, training courses, and conferences. Additional benefits available - see our full benefits list. Equal Opportunity Employer Diversity and inclusion are a priority for us. We are an equal opportunity employer and will consider all applicants without regard to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, veteran status, neurodiversity, or disability status.

About the job We are developing a highly scalable media intelligence platform that processes, analyzes, and structures large volumes of multimedia content across text, image, video, and audio. As a Senior Applied ML Engineer, you will architect and build the core backend systems that power media ingestion, processing workflows, metadata generation, AI-based analysis, semantic search, and retrieval across large media libraries. We are looking for a Senior Applied ML Engineer who can design, implement, optimize, and evaluate a production grade moderation pipeline using open source models. This role requires deep backend engineering expertise, strong system design capability, and practical experience integrating AI/ML systems into production workflows. You will work on complex media processing pipelines, video/audio analysis, OCR, speech to text, embedding generation, vector search, multimodal model integrations, and high throughput asynchronous workloads. You will collaborate closely with engineering leadership to define backend architecture, improve reliability and scalability, and guide other engineers in delivering secure, observable, and high performance systems. Responsibilities Backend Architecture & System Ownership Architect, build, and operate scalable backend services for a media intelligence platform, with a focus on clean, maintainable, and production ready systems. Own critical backend components end to end, from system design and API contracts through implementation, deployment, monitoring, and iteration. Drive architectural decisions across APIs, processing pipelines, distributed compute, storage, search, observability, cloud infrastructure, and model serving workflows. Design data models and storage patterns for media assets, generated metadata, embeddings, processing jobs, model outputs, search indexes, and audit trails. Design high throughput media ingestion and processing pipelines for large volumes of video, audio, image, and text content. Build distributed, event driven workflows for media processing using queues and pub/sub systems such as SQS, Kafka, Pub/Sub, or equivalent technologies. Implement reliable asynchronous processing patterns, including retries, idempotency, dead letter queues, backpressure handling, and fault tolerant job execution. AI/ML Integration & Model Workflows Lead the development and optimization of metadata extraction, content analysis, scene detection, transcription, embedding generation, and multimodal AI inference workflows. Integrate and optimize AI/ML services within backend workflows, including model APIs, embedding pipelines, OCR, speech to text, scene analysis, multimodal inference, batching, caching, and fallback strategies. Collaborate with ML engineers, data scientists, or external model providers to benchmark models, compare quality/latency trade offs, and safely roll out model upgrades. Model Serving & Performance Optimization Optimize AI/ML inference workflows for latency, throughput, reliability, and cost across both real time and batch processing paths. Work with model serving systems such as vLLM, Triton, TGI, SageMaker, Vertex AI, or custom inference services to improve batching, concurrency, warmup behavior, timeout handling, autoscaling, and GPU utilization. Evaluate and apply practical model optimization techniques such as quantization, model distillation, batching, caching, prompt optimization, and routing to smaller or cheaper models where appropriate. Design and maintain vector search and indexing systems using technologies such as Pinecone, Weaviate, Qdrant, Elastic Vectors, FAISS, pgvector, or similar tools. Build retrieval workflows that support semantic search, similarity matching, duplicate detection, media discovery, and structured metadata search. Monitor model and system performance in production, including API latency, queue depth, processing time, model error rates, GPU utilization, confidence distributions, drift signals, and cost per processed item. Infrastructure, Reliability & Observability Deploy and operate systems on AWS, GCP, Azure, or equivalent cloud platforms, including compute, storage, networking, queues, model serving infrastructure, and monitoring systems. Ensure system reliability through logging, metrics, tracing, alerting, dashboards, operational runbooks, and incident response best practices. Collaboration & Engineering Leadership Collaborate with product, design, data, and ML teams to deliver media rich, AI powered product features. Mentor junior and mid level engineers, support technical planning, review designs, and raise engineering quality across the team. Participate in code reviews, documentation, technical planning, and continuous improvement of engineering practices. Ensure code quality through testing, peer review, clear documentation, and maintainable implementation patterns. Education & Experience Bachelor's degree in Computer Science, Engineering, or equivalent practical experience. 5-7+ years of backend engineering experience, ideally building scalable distributed systems, media platforms, data pipelines, or high throughput backend services. Prior experience owning major backend modules end to end, including architecture, implementation, deployment, monitoring, and production operations. 3+ years of experience integrating AI/ML inference systems into backend workflows, including model APIs, embedding pipelines, OCR, speech to text, scene detection, or multimodal model outputs. Hands on experience creating AI powered processing pipelines for image, video, audio, or text analysis. Practical experience with production model optimization, especially for image, video, embedding, or multimodal models, including batching, caching, quantization, prompt optimization, routing strategies, latency reduction, and cost optimization. Prior experience with vector search, semantic search, media retrieval, or similarity matching systems is strongly preferred. Experience mentoring engineers, leading technical discussions, and influencing architectural decisions across backend, infrastructure, and AI/ML workflows. Technical Skills Strong expertise in Python and/or Node.js with deep understanding of building scalable RESTful APIs and backend architectures. Experience with HuggingFace transformers ecosystem and deep learning frameworks such as PyTorch and TensorFlow. Strong experience with SQL/NoSQL databases, schema design, and data modeling. Preferred exposure to distributed systems, microservices, asynchronous processing, and event driven patterns with SQS, Pub/Sub, Kafka, or other queueing/pub sub systems. Experience deploying production systems on AWS, GCP, or similar cloud platforms. Knowledge of infrastructure patterns (compute, storage, networking, observability). AI/ML Integration Experience orchestrating embedding generation, scene detection, OCR, speech to text, image classification, video analysis, and multimodal model integrations. Experience optimizing inference workflows for latency, throughput, reliability, and cost. Experience working with scalable and optimized inference settings, including tuning sampling parameters, managing output length formats, and configuring reasoning related behaviors. Familiarity with practical model optimization techniques such as batching, caching, quantization, model distillation, prompt optimization, fallback routing, and use of smaller models where appropriate. Experience working with model serving systems such as vLLM, Triton, TGI, SageMaker, Vertex AI, or custom inference services is preferred. Experience working with LLM and multi modal evaluation and benchmarking frameworks and domain specific benchmarks with the ability to interpret results and optimize model performance accordingly. System Design & Architecture Preferred understanding of distributed systems, scaling patterns, and performance engineering. Ability to design modular, maintainable, and efficient architectures. Experience with API versioning, modularization, and designing long running workflows. Understanding of performance bottlenecks and low latency backend patterns.