13 jobs found

Senior Machine Learning Engineer - Behavioural Modeling & Threat Detection - £150,000 - £180,000 - Fully Remote UK BASED CANDIDATES ONLY My client is looking for an experienced Machine Learning Engineer ready to play a pivotal role in shaping the technical direction of their behavioural modelling and threat detection systems. This position offers the opportunity to influence not just their engineering roadmap, but how they fundamentally approach solving complex, real-world security challenges with data. You'll work at the intersection of data science, ML infrastructure, and product innovation, leading efforts to build and evolve ML-driven capabilities, while also ensuring the reliability and scalability of their models in production environments. What You'll Do Spearhead the design and refinement of machine learning models focused on understanding behaviour patterns and identifying cybersecurity anomalies. Partner with product, engineering, and domain experts to translate strategic goals and customer needs into practical, scalable ML solutions. Drive model development end-to-end, from exploratory analysis, feature design, and prototyping to validation and deployment. Collaborate with platform and infra teams to operationalize models and ship ML-powered features into production. Continuously assess and iterate on production models, balancing long-term ML strategy with tactical improvements. Champion code quality, observability, and resilience within their ML systems through reviews and hands-on contributions. Help shape their internal ML standards and practices, ensuring they stay ahead of industry advancements. Offer technical mentorship and be a thought partner to colleagues across data, ML, and engineering disciplines. What We're Looking For Hands-on experience in developing and deploying machine learning models at scale. Deep familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed data tools (Spark, etc.). Strong written and verbal communication skills, especially in cross-functional contexts. Bonus Experience (Nice to Have) Exposure to large language models (LLMs) or foundational model adaptation. Previous work in cybersecurity, anomaly detection, or behavioural analytics. Familiarity with orchestration frameworks (Airflow or similar). Experience with scalable ML systems, pipelines, or real-time data processing. Advanced degree or equivalent experience in ML/AI research or applied science. Cloud platform proficiency (AWS, GCP, Azure). If this sounds like something you would be interested in, please apply with your latest CV, a number to reach you on and I will be in touch. Alternatively, you can email me at . RSG Plc is acting as an Employment Agency in relation to this vacancy.

Machine Learning Engineer Up to £70K DOE Hybrid – London (2 days per week onsite) My client is looking for a Junior to Mid-Level Machine Learning Engineer to take ownership of the infrastructure and services that power machine learning systems in production. In this role, you’ll act as a bridge between data science and engineering, ensuring robust, scalable, and low-latency deployment of models that serve millions of requests per day. You’ll be responsible for building and maintaining Python microservices, leveraging modern DevOps practices and tooling to support rapid, reliable delivery. With sub-second response times and a high-throughput environment (2M+ requests/day), this is a high-impact role that blends software engineering, DevOps, and MLOps at scale. Key Responsibilities * Design, develop, and maintain Python microservices for serving machine learning models * Collaborate with Data Scientists to deploy, monitor, and support models in production * Implement and manage CI/CD pipelines using Azure DevOps * Support containerized deployments with Kubernetes and Docker * Ensure high performance, fault-tolerant, and secure infrastructure * Promote code quality, testing standards, and scalable architecture * Proactively identify infrastructure improvements and lead implementation Requirements * 2 + years of experience in Software Engineering, DevOps, or Data Engineering * Strong Python skills with experience in microservices and web frameworks * Solid understanding of CI/CD, ideally using Azure DevOps * Familiarity with containerized environments (Docker/Kubernetes) * Exposure to Data Science or Machine Learning concepts * Experience operating in high-throughput environments * Independent, curious, and driven by continuous improvement * Effective communicator with the ability to bridge data science and engineering teams Why Join? You’ll be joining a company with strong business performance and ambitious plans for data-driven growth. This is a rare opportunity to take technical ownership of real-time machine learning infrastructure and play a key role in scaling systems that make an immediate business impact

Site Name: UK - Hertfordshire - Stevenage, USA - Connecticut - Hartford, USA - Delaware - Dover, USA - Maryland - Rockville, USA - Massachusetts - Cambridge, USA - Massachusetts - Waltham, USA - New Jersey - Trenton, USA - Pennsylvania - Upper Providence Posted Date: Jun 6 2022 The mission of the Data Science and Data Engineering (DSDE) organization within GSK Pharmaceuticals R&D is to get the right data, to the right people, at the right time. TheData Framework and Opsorganization ensures we can do this efficiently, reliably, transparently, and at scale through the creation of a leading-edge, cloud-native data services framework. We focus heavily on developer experience, on strong, semantic abstractions for the data ecosystem, on professional operations and aggressive automation, and on transparency of operations and cost. Achieving delivery of the right data to the right people at the right time needs design and implementation of data flows and data products which leverage internal and external data assets and tools to drive discovery and development is a key objective for the Data Science and Data Engineering (DSDE) team within GSK's Pharmaceutical R&D organization. There are five key drivers for this approach, which are closely aligned with GSK's corporate priorities of Innovation, Performance and Trust: Automation of end-to-end data flows :Faster and reliable ingestion of high throughput data in genetics, genomics and multi-omics, to extract value of investments in new technology (instrument to analysis-ready data in Enabling governance by design of external and internal data :with engineered practical solutions for controlled use and monitoring Innovative disease-specific and domain-expert specific data products : to enable computational scientists and their research unit collaborators to get faster to key insights leading to faster biopharmaceutical development cycles. Supporting e2ecode traceability and data provenance :Increasing assurance of data integrity through automation, integration. Improving engineering efficiency :Extensible, reusable, scalable,updateable,maintainable, virtualized traceable data and code would be driven by data engineering innovation and better resource utilization. We are looking for experienced Senior DevOps Engineers to join our growing Data Ops team. As a Senior Dev Ops Engineer is a highly technical individual contributor, building modern, cloud-native, DevOps-first systems for standardizing and templatizingbiomedical and scientificdata engineering, with demonstrable experience across the following areas: Deliver declarative components for common data ingestion, transformation and publishing techniques Define and implement data governance aligned to modern standards Establish scalable, automated processes for data engineering teams across GSK Thought leader and partner with wider DSDE data engineering teams to advise on implementation and best practices Cloud Infrastructure-as-Code Define Service and Flow orchestration Data as a configurable resource(including configuration-driven access to scientific data modelling tools) Observability (monitoring, alerting, logging, tracing, etc.) Enable quality engineering through KPIs and code coverage and quality checks Standardise GitOps/declarative software development lifecycle Audit as a service Senior DevOpsEngineers take full ownership of delivering high-performing, high-impactbiomedical and scientificdataopsproducts and services, froma description of apattern thatcustomer Data Engineers are trying touseall the way through tofinal delivery (and ongoing monitoring and operations)of a templated project and all associated automation. They arestandard-bearers for software engineering and quality coding practices within theteam andareexpected to mentor more junior engineers; they may even coordinate the work of more junior engineers on a large project.Theydevise useful metrics for ensuring their services are meeting customer demand and having animpact anditerate to deliver and improve on those metrics in an agile fashion. Successful Senior DevOpsEngineers are developing expertise with the types of data and types of tools that are leveraged in the biomedical and scientific data engineering space, andhas the following skills and experience(withsignificant depth in one or more of these areas): Demonstrable experience deploying robust modularised/container-based solutions to production (ideally GCP) and leveraging the Cloud NativeComputing Foundation (CNCF) ecosystem Significant depth in DevOps principles and tools (e.g.GitOps, Jenkins,CircleCI, Azure DevOps, etc.), and how to integrate these tools with other productivity tools (e.g. Jira, Slack, Microsoft Teams) to build a comprehensive workflow Programming in Python. Scala orGo Embedding agile software engineering (task/issue management, testing, documentation, software development lifecycle, source control, etc.) Leveraging major cloud providers, both via Kubernetesorvia vendor-specific services Authentication and Authorization flows and associated technologies (e.g.OAuth2 + JWT) Common distributed data tools (e.g.Spark, Hive) The DSDE team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we're motivated to make this a great place to work. Our leaders will be committed to your career and development from day one. Why you? Basic Qualifications: We are looking for professionals with these required skills to achieve our goals: Masters in Computer Science with a focus in Data Engineering, DataOps, DevOps, MLOps, Software Engineering, etc, plus 5 years job experience (or PhD plus 3 years job experience) Experience with DevOps tools and concepts (e.g. Jira, GitLabs / Jenkins / CircleCI / Azure DevOps /etc.)Excellent with common distributed data tools in a production setting (Spark, Kafka, etc) Experience with specialized data architecture (e.g. optimizing physical layout for access patterns, including bloom filters, optimizing against self-describing formats such as ORC or Parquet, etc.) Experience with search / indexing systems (e.g. Elasticsearch) Expertise with agile development in Python, Scala, Go, and/or C++ Experience building reusable components on top of the CNCF ecosystem including Kubernetes Metrics-first mindset Experience mentoring junior engineers into deep technical expertise Preferred Qualifications: If you have the following characteristics, it would be a plus: Experience with agile software development Experience with building and designing a DevOps-first way of working Experience with building reusable components on top of the CNCF ecosystem including Kubernetes (or similar ecosystem ) LI-GSK Why GSK? Our values and expectationsare at the heart of everything we do and form an important part of our culture. These include Patient focus, Transparency, Respect, Integrity along with Courage, Accountability, Development, and Teamwork. As GSK focuses on our values and expectations and a culture of innovation, performance, and trust, the successful candidate will demonstrate the following capabilities: Operating at pace and agile decision making - using evidence and applying judgement to balance pace, rigour and risk. Committed to delivering high-quality results, overcoming challenges, focusing on what matters, execution. Continuously looking for opportunities to learn, build skills and share learning. Sustaining energy and wellbeing Building strong relationships and collaboration, honest and open conversations. Budgeting and cost consciousness As a company driven by our values of Patient focus, Transparency, Respect and Integrity, we know inclusion and diversity are essential for us to be able to succeed. We want all our colleagues to thrive at GSK bringing their unique experiences, ensuring they feel good and to keep growing their careers. As a candidate for a role, we want you to feel the same way. As an Equal Opportunity Employer, we are open to all talent. In the US, we also adhere to Affirmative Action principles. This ensures that all qualified applicants will receive equal consideration for employment without regard to race/ethnicity, colour, national origin, religion, gender, pregnancy, marital status, sexual orientation, gender identity/expression, age, disability, genetic information, military service, covered/protected veteran status or any other federal, state or local protected class ( US only). We believe in an agile working culture for all our roles. If flexibility is important to you, we encourage you to explore with our hiring team what the opportunities are. Please don't hesitate to contact us if you'd like to discuss any adjustments to our process which might help you demonstrate your strengths and capabilities. You can either call us on , or send an email As you apply, we will ask you to share some personal information which is entirely voluntary..... click apply for full job details

Site Name: UK - Hertfordshire - Stevenage, USA - Connecticut - Hartford, USA - Delaware - Dover, USA - Maryland - Rockville, USA - Massachusetts - Waltham, USA - Pennsylvania - Upper Providence, Warren NJ Posted Date: Aug The mission of the Data Science and Data Engineering (DSDE) organization within GSK Pharmaceuticals R&D is to get the right data, to the right people, at the right time. TheData Framework and Opsorganization ensures we can do this efficiently, reliably, transparently, and at scale through the creation of a leading-edge, cloud-native data services framework. We focus heavily on developer experience, on strong, semantic abstractions for the data ecosystem, on professional operations and aggressive automation, and on transparency of operations and cost. Achieving delivery of the right data to the right people at the right time needs design and implementation of data flows and data products which leverage internal and external data assets and tools to drive discovery and development is a key objective for the Data Science and Data Engineering (DS D E) team within GSK's Pharmaceutical R&D organisation . There are five key drivers for this approach, which are closely aligned with GSK's corporate priorities of Innovation, Performance and Trust: Automation of end-to-end data flows: Faster and reliable ingestion of high throughput data in genetics, genomics and multi-omics, to extract value of investments in new technology (instrument to analysis-ready data in Enabling governance by design of external and internal data: with engineered practical solutions for controlled use and monitoring Innovative disease-specific and domain-expert specific data products : to enable computational scientists and their research unit collaborators to get faster to key insights leading to faster biopharmaceutical development cycles. Supporting e2 e code traceability and data provenance: Increasing assurance of data integrity through automation, integration Improving engineering efficiency: Extensible, reusable, scalable,updateable,maintainable, virtualized traceable data and code would b e driven by data engineering innovation and better resource utilization. We are looking for an experienced Sr. Data Ops Engineer to join our growing Data Ops team. As a Sr. Data Ops Engineer is a highly technical individual contributor, building modern, cloud-native, DevOps-first systems for standardizing and templatizingbiomedical and scientificdata engineering, with demonstrable experience across the following areas : Deliver declarative components for common data ingestion, transformation and publishing techniques Define and implement data governance aligned to modern standards Establish scalable, automated processes for data engineering team s across GSK Thought leader and partner with wider DSDE data engineering teams to advise on implementation and best practices Cloud Infrastructure-as-Code D efine Service and Flow orchestration Data as a configurable resource(including configuration-driven access to scientific data modelling tools) Ob servabilty (monitoring, alerting, logging, tracing, ...) Enable quality engineering through KPIs and c ode coverage and quality checks Standardise GitOps /declarative software development lifecycle Audit as a service Sr. DataOpsEngineerstake full ownership of delivering high-performing, high-impactbiomedical and scientificdataopsproducts and services, froma description of apattern thatcustomer Data Engineers are trying touseall the way through tofinal delivery (and ongoing monitoring and operations)of a templated project and all associated automation. They arestandard-bearers for software engineering and quality coding practices within theteam andareexpected to mentor more junior engineers; they may even coordinate the work of more junior engineers on a large project.Theydevise useful metrics for ensuring their services are meeting customer demand and having animpact anditerate to deliver and improve on those metrics in an agile fashion. A successfulSr.DataOpsEngineeris developing expertise with the types of data and types of tools that are leveraged in the biomedical and scientific data engineering space, andhas the following skills and experience(withsignificant depth in one or more of these areas): Demonstrable experience deploying robust modularised/ container based solutions to production (ideally GCP) and leveraging the Cloud NativeComputing Foundation (CNCF) ecosystem Significant depth in DevOps principles and tools ( e.g. GitOps , Jenkins, CircleCI , Azure DevOps, ...), and how to integrate these tools with other productivity tools (e.g. Jira, Slack, Microsoft Teams) to build a comprehensive workflow P rogramming in Python. Scala or Go Embedding agile s oftware engineering ( task/issue management, testing, documentation, software development lifecycle, source control, ) Leveraging major cloud providers, both via Kubernetes or via vendor-specific services Authentication and Authorization flows and associated technologies ( e.g. OAuth2 + JWT) Common distributed data tools ( e.g. Spark, Hive) The DSDE team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we're motivated to make this a great place to work. Our leaders will be committed to your career and development from day one. Why you? Basic Qualifications: Bachelors degree in Computer Science with a focus in Data Engineering, DataOps, DevOps, MLOps, Software Engineering, etc, plus 7 years job experience or Masters degree with 5 Years of experience (or PhD plus 3 years job experience) Deep experience with DevOps tools and concepts ( e.g. Jira, GitLabs / Jenkins / CircleCI / Azure DevOps / ...) Excellent with common distributed data tools in a production setting (Spark, Kafka, etc) Experience with specialized data architecture ( e.g. optimizing physical layout for access patterns, including bloom filters, optimizing against self-describing formats such as ORC or Parquet, etc) Experience with search / indexing systems ( e.g. Elasticsearch) Deep expertise with agile development in Python, Scala, Go, and/or C++ Experience building reusable components on top of the CNCF ecosystem including Kubernetes Metrics-first mindset Experience mentoring junior engineers into deep technical expertise Preferred Qualifications: If you have the following characteristics, it would be a plus: Experience with agile software development Experience building and designing a DevOps-first way of working Demonstrated experience building reusable components on top of the CNCF ecosystem including Kubernetes (or similar ecosystem ) LI-GSK Why GSK? Our values and expectations are at the heart of everything we do and form an important part of our culture. These include Patient focus, Transparency, Respect, Integrity along with Courage, Accountability, Development, and Teamwork. As GSK focuses on our values and expectations and a culture of innovation, performance, and trust, the successful candidate will demonstrate the following capabilities: Operating at pace and agile decision making - using evidence and applying judgement to balance pace, rigour and risk. Committed to delivering high-quality results, overcoming challenges, focusing on what matters, execution. Continuously looking for opportunities to learn, build skills and share learning. Sustaining energy and wellbeing Building strong relationships and collaboration, honest and open conversations. Budgeting and cost consciousness As a company driven by our values of Patient focus, Transparency, Respect and Integrity, we know inclusion and diversity are essential for us to be able to succeed. We want all our colleagues to thrive at GSK bringing their unique experiences, ensuring they feel good and to keep growing their careers. As a candidate for a role, we want you to feel the same way. As an Equal Opportunity Employer, we are open to all talent. In the US, we also adhere to Affirmative Action principles. This ensures that all qualified applicants will receive equal consideration for employment without regard to neurodiversity, race/ethnicity, colour, national origin, religion, gender, pregnancy, marital status, sexual orientation, gender identity/expression, age, disability, genetic information, military service, covered/protected veteran status or any other federal, state or local protected class ( US only). We believe in an agile working culture for all our roles. If flexibility is important to you, we encourage you to explore with our hiring team what the opportunities are. Please don't hesitate to contact us if you'd like to discuss any adjustments to our process which might help you demonstrate your strengths and capabilities. You can either call us on , or send an email As you apply, we will ask you to share some personal information which is entirely voluntary..... click apply for full job details

Site Name: USA - Pennsylvania - Upper Providence, UK - Hertfordshire - Stevenage, UK - London - Brentford, USA - Pennsylvania - Philadelphia Posted Date: Oct The mission of the Data Science and Data Engineering (DSDE) organization within GSK Pharmaceuticals R&D is to get the right data, to the right people, at the right time. TheData Framework and Opsorganization ensures we can do this efficiently, reliably, transparently, and at scale through the creation of a leading-edge, cloud-native data services framework. We focus heavily on developer experience, on strong, semantic abstractions for the data ecosystem, on professional operations and aggressive automation, and on transparency of operations and cost. We are looking for a skilled Data Ops Engineer II to join our growing team. The Data Ops team acceleratesbiomedicaland scientificdata product development and ensures consistent, professional-grade operations for the Data Science and Engineering organization by building templated projects (code repository plus DevOps pipelines) for various Data Science/Data Engineering architecture patternsin the challenging biomedical data space.A Data Ops Engineer IIknows the metrics desired for their tools andservices anditerates to deliver and improve on those metrics in an agile fashion. A Data Ops Engineer II is a highly technical individual contributor, building modern, cloud-native systems for standardizing and templatizing data engineering, such as: Standardized physical storage and search / indexing systems Schema management (data + metadata + versioning + provenance + governance) API semantics and ontology management Standard API architectures Kafka + standard streaming semantics Standard components for publishing data to file-based, relational, and other sorts of data stores Metadata systems Tooling for QA / evaluation Audit as a Service Additional responsibilities also include: Given a well-specified data framework problem, implement end-to-end solutionsusing appropriate programming languages(e.g.Python,Scala, or Go), open-source tools (e.g.Spark,Elasticsearch, ...), and cloud vendor-provided tools (e.g.Amazon S3) Leverage tools provided by Tech (e.g.infrastructure as code, CloudOps,DevOps, logging / alerting, ...) in delivery ofsolutions Write proper documentation in code as well as in wikis/other documentationsystems Writefantastic code along with theproper unit, functional, and integration tests for code and services to ensurequality Stayup to datewith developments in theopen-sourcecommunity around data engineering, data science, and similartooling The DSDE team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we're motivated to make this a great place to work. Our leaders will be committed to your career and development from day one. Why you? Basic Qualifications: We are looking for professionals with these required skills to achieve our goals: Master's in Computer Science with a focus in Data Engineering, DataOps, DevOps, MLOps, Software Engineering and 2+ years of experience OR PhD in Computer Science Demonstrated experience with software engineering (testing, documentation, software development lifecycle, source control, ... Experience with DevOps tools and concepts (e.g. Jira, GitLabs / Jenkins / CircleCI / Azure DevOps / ...) Experience with common distributed data tools in a production setting (Spark, Kafka, etc) Experience with basics of search engines/indexing (e.g. Elasticsearch, Lucene) Demonstrated experience in writing Python, Scala, Go, and/or C++ Preferred Qualifications: If you have the following characteristics, it would be a plus: Comfort with specialized data architecture (e.g. optimizing physical layout for access patterns, including bloom filters, optimizing against self-describing formats such as ORC or Parquet, etc) Experience with the CNCF ecosystem / Kubernetes Comfort with search/indexing systems (e.g. Elasticsearch) Experience with schema tools/schema management (Avro, Protobuf) Why GSK? Our values and expectations are at the heart of everything we do and form an important part of our culture. These include Patient focus, Transparency, Respect, Integrity along with Courage, Accountability, Development, and Teamwork. As GSK focuses on our values and expectations and a culture of innovation, performance, and trust, the successful candidate will demonstrate the following capabilities: Operating at pace and agile decision making - using evidence and applying judgement to balance pace, rigour and risk. Committed to delivering high-quality results, overcoming challenges, focusing on what matters, execution. Continuously looking for opportunities to learn, build skills and share learning. Sustaining energy and wellbeing Building strong relationships and collaboration, honest and open conversations. Budgeting and cost consciousness LI-GSK If you require an accommodation or other assistance to apply for a job at GSK, please contact the GSK Service Centre at 1- (US Toll Free) or +1 (outside US). GSK is an Equal Opportunity Employer and, in the US, we adhere to Affirmative Action principles. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, national origin, religion, sex, pregnancy, marital status, sexual orientation, gender identity/expression, age, disability, genetic information, military service, covered/protected veteran status or any other federal, state or local protected class. At GSK, the health and safety of our employees are of paramount importance. As a science-led healthcare company on a mission to get ahead of disease together, we believe that supporting vaccination against COVID-19 is the single best thing we can do in the US to ensure the health and safety of our employees, complementary workers, workplaces, customers, consumers, communities, and the patients we serve. GSK has made the decision to require all US employees to be fully vaccinated against COVID-19, where allowed by state or local law and where vaccine supply is readily available. The only exceptions to this requirement are employees who are approved for an accommodation for religious, medical or disability-related reasons. Important notice to Employment businesses/ Agencies GSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site. Please note that if you are a US Licensed Healthcare Professional or Healthcare Professional as defined by the laws of the state issuing your license, GSK may be required to capture and report expenses GSK incurs, on your behalf, in the event you are afforded an interview for employment. This capture of applicable transfers of value is necessary to ensure GSK's compliance to all federal and state US Transparency requirements. For more information, please visit GSK's Transparency Reporting For the Record site.