At a Glance
- Tasks: Lead data science initiatives to optimise credit performance and drive sustainable growth.
- Company: Join a dynamic fintech company focused on innovative credit solutions.
- Benefits: Competitive salary, flexible working options, and opportunities for professional development.
- Other info: Collaborative environment with opportunities to mentor and lead a talented team.
- Why this job: Make a real impact by shaping credit risk strategies and influencing business decisions.
- Qualifications: Strong analytical skills in SQL and Python, with experience in credit risk analytics.
The predicted salary is between 80000 - 100000 € per year.
Requirements
- Strong analytical skills with fluency in SQL and Python
- Ability to interpret and communicate model performance metrics, feature importance, and confidence intervals
- Background in collaborating with decision science or data science teams on feature engineering and model evaluation
- Experience conducting large scale A/B experiments and interpreting results to drive product and business decisions
- Fluent in credit portfolio metrics – e.g. arrears buckets, roll rates, loss rate, yield/marginal loss – and how they tie to unit economics and P&L
- Experience building and maintaining performance monitoring systems and alerting frameworks
- Hands-on experience working with predictive models (e.g. credit, fraud, marketing), including interpreting metrics like AUC/Gini, calibration, PSI/CSI, drift
- Experience collaborating with commercial and finance teams on business metric forecasting
- Track record of taking analyses all the way through to shipped changes and measurable impact
- Experience managing or mentoring analysts and structuring team output for maximum impact
- Skilled at translating technical analysis into actionable recommendations for senior stakeholders
- Able to balance short-term monitoring needs with long-term framework building
- (Desirable) Familiarity with short-term or revolving credit products
- (Desirable) Experience working with both UK and US regulatory frameworks
What the job involves
We’re looking for a Head of Data Science to lead the measurement, monitoring, and optimisation of Cleo’s credit performance. This is a high-impact role at the heart of how we manage risk and drive sustainable growth. You’ll own the frameworks, metrics, and deep-dive analyses that keep our credit products healthy and ensure that when performance shifts, we understand why and act quickly.
Sitting within the Risk & Payments pillar, you’ll lead a team of analysts to deliver clear, actionable insights across arrears, yield, LTV, and portfolio performance, working closely with Credit Policy, Decision Science, Product, and Finance. You’ll be setting the measurement standards for the company, diagnosing portfolio trends, and shaping how we evaluate and implement policy changes.
This role is ideal for someone who thrives at the intersection of data, systems thinking, and stakeholder influence. You’ll be equally comfortable explaining SHAP outputs to a data scientist, talking loss economics with finance, and summarising driver analysis for senior leadership.
Key Responsibilities
- Own the design and maintenance of Cleo’s credit risk metric framework, including arrears, default, yield, LTV, and marginal loss rates
- Build and maintain dashboards and alerts for early detection of arrears, roll-rate shifts, and decisioning anomalies
- Ensure definitions, thresholds, and escalation processes are consistent, documented, and used company-wide
- Partner with the Risk Modelling team to turn model health metrics (AUC, PSI, calibration, feature drift) into clear recommendations for policy or product changes
- Detect and diagnose feature shift or concept drift, ensuring model inputs remain valid and predictive
- Lead investigations into performance deviations, separating model-driven changes from macro or operational causes
- Lead root cause analysis for deterioration in key metrics such as arrears spikes or yield compression
- Own investigations from question - analysis - recommendation, and present your work to Risk, Product, and Leadership
- Deliver driver analysis (SHAP, feature importance, decomposition) for changes in portfolio performance
- Quantify the risk-adjusted impact of new or changing product features on portfolio health
- Design and analyse multivariate experiments on underwriting, pricing, or repayment flows, and translate results into actionable risk strategies
- Partner with Credit Policy and Decision Science to design robust evaluation frameworks for policy changes
- Quantify the marginal impact of policy shifts, controlling for macro or seasonal effects
- Support elasticity and profitability modelling to optimise amounts, pricing, and feature-level decisioning
- Manage and mentor Credit Data Scientists/Analysts, setting clear priorities and deliverables
- Translate complex analytical findings into clear business implications for senior leadership
- Build strong cross-functional relationships with Commercial, Product, Data Science, and Finance
Head of Data Science (Credit Risk Analytics) in London employer: Deepstreamtech
Cleo is an exceptional employer, offering a dynamic work environment where innovation meets collaboration. As the Head of Data Science, you'll lead a talented team in a high-impact role that drives sustainable growth and risk management, all while enjoying a culture that prioritises employee development and cross-functional partnerships. With competitive benefits and opportunities for professional growth, Cleo is committed to fostering a workplace where your contributions are valued and impactful.
StudySmarter Expert Advice🤫
We think this is how you could land Head of Data Science (Credit Risk Analytics) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving SQL and Python. This will give potential employers a taste of what you can do and how you can contribute to their team.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of credit risk metrics and model performance. Be ready to discuss your past experiences and how they relate to the role. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Head of Data Science (Credit Risk Analytics) in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your analytical skills, especially your fluency in SQL and Python. We want to see how you interpret model performance metrics and communicate them effectively, so don’t hold back on showcasing your technical prowess!
Tell Your Story:When you describe your experience, focus on specific projects where you collaborated with decision science or data science teams. We love seeing how you've contributed to feature engineering and model evaluation, so share those success stories!
Be Results-Driven:We’re all about impact here at StudySmarter, so make sure to include examples of how your analyses led to real changes. Whether it’s through A/B testing or forecasting, show us how your work has driven product and business decisions.
Keep It Clear and Concise:While we appreciate detail, clarity is key! Make your application easy to read and understand. Use straightforward language to translate your complex analyses into actionable recommendations, especially for senior stakeholders.
How to prepare for a job interview at Deepstreamtech
✨Master the Metrics
Make sure you’re fluent in credit portfolio metrics like arrears buckets, roll rates, and loss rates. Be ready to discuss how these metrics tie into unit economics and P&L during your interview. This shows you understand the financial implications of your work.
✨Showcase Your Technical Skills
Brush up on your SQL and Python skills before the interview. Prepare to demonstrate your ability to interpret model performance metrics and communicate them effectively. You might even be asked to solve a problem on the spot, so practice coding challenges related to data science.
✨Prepare for Collaboration Questions
Expect questions about your experience collaborating with decision science or finance teams. Have examples ready that showcase your ability to translate technical analysis into actionable recommendations for senior stakeholders. Highlight any past experiences where your insights led to measurable impact.
✨Demonstrate Leadership and Mentoring
As a Head of Data Science, you’ll need to manage and mentor analysts. Be prepared to discuss your leadership style and how you structure team output for maximum impact. Share specific examples of how you’ve guided team members in the past and the results that followed.
