Data Scientist

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.