Kroo Ltd
Kroo Bank is charting the future of banking through technology, data, and innovation. As a digital first bank, we use data science to help us make smarter decisions, improve customer outcomes, and build products that customers trust and love. The rapid pace of change within fintech creates exciting opportunities to apply advanced analytics, machine learning, and experimentation to real business challenges. As a Data Scientist, you will play a key role in helping Kroo use data more effectively across a wide range of business areas, partnering with teams across Product, Risk, Operations, Compliance, and Engineering. This role is responsible for building, evaluating, and deploying data science solutions that support strategic decision making and improve customer experiences. You will work on high impact initiatives across areas such as credit risk, fraud prevention, customer engagement, and operational efficiency, helping the business make informed decisions through robust analysis, experimentation, and modelling. How you'll contribute: Build and iterate on statistical and machine learning models to solve business problems across areas such as credit risk, fraud, customer engagement, and operational efficiency. Partner with stakeholders to define problem statements, success metrics, data requirements, and practical implementation plans. Conduct data exploration and feature engineering to uncover drivers of outcomes and improve model performance and interpretability. Develop robust evaluation frameworks, including appropriate baselines, validation strategies, monitoring metrics, and model performance reporting. Support deployment of models into production in collaboration with Engineering, contributing to reproducible pipelines and model documentation. Monitor models in production, identify performance drift, propose improvements, and support ongoing recalibration or retraining where required. Apply probability and statistical inference to design experiments, interpret results, and provide clear recommendations to stakeholders. Contribute to high quality data practices by identifying data quality issues, supporting cleaning and normalisation approaches, and defining standards for reliable datasets. Write maintainable, well tested Python code using common data science libraries, and follow engineering best practices appropriate for production systems. Use SQL and dbt to extract, transform, and validate data for analysis and modelling, ensuring traceability and reliability of outputs. Collaborate with Risk, Compliance, and Audit stakeholders to ensure data science work is appropriately governed, documented, and aligned with regulatory expectations. Support continuous improvement across data science methodologies, tooling, and ways of working. Required skills and behaviours: Experience building and evaluating statistical and machine learning models in a commercial environment. Strong analytical and problem solving skills with the ability to translate business challenges into practical data science solutions. Ability to conduct basic data collection by independently sourcing and defining required datasets, partnering with stakeholders to clarify data needs and ensure appropriate coverage and traceability. Ability to perform data cleaning effectively by independently applying robust cleaning approaches, proactively identifying data quality issues, and contributing to improving data reliability and standards. Ability to conduct basic data analysis by independently performing exploratory analysis and statistical investigation, translating findings into clear insights and actionable recommendations. Strong programming fundamentals with experience writing maintainable Python code for analysis and modelling, contributing to shared codebases through good practices, testing, and documentation. Experience using SQL and dbt to extract, transform, validate, and analyse data. Ability to apply visualisation techniques to produce clear, purposeful visualisations and model performance summaries that support decision making across technical and non technical audiences. Ability to communicate effectively by explaining complex analytical concepts clearly and tailoring messages to a wide range of stakeholders. Strong attention to detail, ensuring outputs are validated, reproducible, and documented in line with governance and compliance requirements. Ability to manage data projects proficiently by planning and delivering work to agreed timelines, managing competing priorities, and contributing positively to team delivery processes. Experience working collaboratively with Product, Risk, Operations, Compliance, and Engineering teams is beneficial. Awareness of model governance, risk management, and regulatory considerations within a financial services environment is advantageous. Hybrid Working At Kroo Bank, we have a hybrid/ flexible policy that gives both individuals and teams a lot of freedom when it comes to using the office space to boost productivity. Our London office is a great resource to collaborate and candidates should be able to attend 1-2 days per week regularly to align with how we work at the moment. Diversity and Inclusion We wholeheartedly uphold our commitment to fostering a diverse and inclusive workplace. Every employee is highly regarded, respected, and supported without any form of judgement or prejudice. We consider Diversity, Equality, and Inclusion as fundamental pillars guiding our path in all aspects of our bank. We also ensure that reasonable adjustments are made available to all candidates throughout the recruitment process.
Kroo Bank is charting the future of banking through technology, data, and innovation. As a digital first bank, we use data science to help us make smarter decisions, improve customer outcomes, and build products that customers trust and love. The rapid pace of change within fintech creates exciting opportunities to apply advanced analytics, machine learning, and experimentation to real business challenges. As a Data Scientist, you will play a key role in helping Kroo use data more effectively across a wide range of business areas, partnering with teams across Product, Risk, Operations, Compliance, and Engineering. This role is responsible for building, evaluating, and deploying data science solutions that support strategic decision making and improve customer experiences. You will work on high impact initiatives across areas such as credit risk, fraud prevention, customer engagement, and operational efficiency, helping the business make informed decisions through robust analysis, experimentation, and modelling. How you'll contribute: Build and iterate on statistical and machine learning models to solve business problems across areas such as credit risk, fraud, customer engagement, and operational efficiency. Partner with stakeholders to define problem statements, success metrics, data requirements, and practical implementation plans. Conduct data exploration and feature engineering to uncover drivers of outcomes and improve model performance and interpretability. Develop robust evaluation frameworks, including appropriate baselines, validation strategies, monitoring metrics, and model performance reporting. Support deployment of models into production in collaboration with Engineering, contributing to reproducible pipelines and model documentation. Monitor models in production, identify performance drift, propose improvements, and support ongoing recalibration or retraining where required. Apply probability and statistical inference to design experiments, interpret results, and provide clear recommendations to stakeholders. Contribute to high quality data practices by identifying data quality issues, supporting cleaning and normalisation approaches, and defining standards for reliable datasets. Write maintainable, well tested Python code using common data science libraries, and follow engineering best practices appropriate for production systems. Use SQL and dbt to extract, transform, and validate data for analysis and modelling, ensuring traceability and reliability of outputs. Collaborate with Risk, Compliance, and Audit stakeholders to ensure data science work is appropriately governed, documented, and aligned with regulatory expectations. Support continuous improvement across data science methodologies, tooling, and ways of working. Required skills and behaviours: Experience building and evaluating statistical and machine learning models in a commercial environment. Strong analytical and problem solving skills with the ability to translate business challenges into practical data science solutions. Ability to conduct basic data collection by independently sourcing and defining required datasets, partnering with stakeholders to clarify data needs and ensure appropriate coverage and traceability. Ability to perform data cleaning effectively by independently applying robust cleaning approaches, proactively identifying data quality issues, and contributing to improving data reliability and standards. Ability to conduct basic data analysis by independently performing exploratory analysis and statistical investigation, translating findings into clear insights and actionable recommendations. Strong programming fundamentals with experience writing maintainable Python code for analysis and modelling, contributing to shared codebases through good practices, testing, and documentation. Experience using SQL and dbt to extract, transform, validate, and analyse data. Ability to apply visualisation techniques to produce clear, purposeful visualisations and model performance summaries that support decision making across technical and non technical audiences. Ability to communicate effectively by explaining complex analytical concepts clearly and tailoring messages to a wide range of stakeholders. Strong attention to detail, ensuring outputs are validated, reproducible, and documented in line with governance and compliance requirements. Ability to manage data projects proficiently by planning and delivering work to agreed timelines, managing competing priorities, and contributing positively to team delivery processes. Experience working collaboratively with Product, Risk, Operations, Compliance, and Engineering teams is beneficial. Awareness of model governance, risk management, and regulatory considerations within a financial services environment is advantageous. Hybrid Working At Kroo Bank, we have a hybrid/ flexible policy that gives both individuals and teams a lot of freedom when it comes to using the office space to boost productivity. Our London office is a great resource to collaborate and candidates should be able to attend 1-2 days per week regularly to align with how we work at the moment. Diversity and Inclusion We wholeheartedly uphold our commitment to fostering a diverse and inclusive workplace. Every employee is highly regarded, respected, and supported without any form of judgement or prejudice. We consider Diversity, Equality, and Inclusion as fundamental pillars guiding our path in all aspects of our bank. We also ensure that reasonable adjustments are made available to all candidates throughout the recruitment process.