We launched Chase UK to revolutionise mobile banking with seamless journeys that our customers love. We’re already trusted by millions in the US and we’re quickly catching up in the UK — but how we do things here is a little different. We’re building the bank of the future with a start-up mentality, meaning you’ll have the opportunity to make a real impact.
As a Data Scientist within JPMorganChase’s International Consumer Bank, you’ll sit in our Business Analytics team and partner closely with Product, Marketing, Operations and Engineering. Your focus will be on using data, experimentation, and lightweight predictive modelling (e.g., churn/retention signals, propensity models). You’ll contribute to analytical direction, deliver high-quality analysis and experimentation, and translate outcomes into clear actions that improve customer experience and business performance.
Our Business Analytics team is at the heart of this venture, focused on getting smart ideas into the hands of our customers. We’re looking for people who are curious, thrive in collaborative squads, and are passionate about practical problem-solving. We work in tribes and squads aligned to products and projects, and depending on your strengths and interests, you’ll have the opportunity to move between them.
While we’re looking for strong professional skills, culture is just as important to us. We value diversity of thought, experience and background, and we want our teams to reflect the communities we serve. Job responsibilities:
- Deliver high-quality analyses and models, and participate in peer reviews to improve analytical quality and communication.
- Partner with cross-functional stakeholders to translate business questions into hypotheses, success metrics, and decision-ready recommendations (with support from senior team members).
- Support measurement for key customer journeys and commercial outcomes, with an emphasis on customer behaviour and profitability (e.g., engagement, retention, cross-sell, unit economics).
- Design, execute, and evaluate experiments (A/B and other controlled tests): define test plans, guardrails, basic sample sizing considerations, and interpret results with appropriate uncertainty.
- Apply statistical methods to quantify impact and drivers (e.g., segmentation, cohorting), and clearly communicate trade-offs and limitations.
- Build, validate, and maintain lightweight predictive models to support decisions (e.g., churn risk/retention targeting, response propensity, customer value proxies), prioritizing interpretability, stability, and measurable lift under guidance and with appropriate review.
- Follow and contribute to best practices for reproducible analysis and experimentation (documentation, code review norms, clear metric definitions).
- Collaborate with cross-functional partners (data engineering, analytics engineering, ML engineering, dashboard developers) to ensure data is fit-for-purpose for analysis/experimentation and insights are operationalised.
- Communicate insights in compelling narratives for both technical and non-technical audiences; influence roadmap and business decisions inform roadmap and business decisions through evidence.
- Required qualifications, capabilities and skills:
- Strong collaboration skills; experience working in cross-functional teams to deliver analytics and experimentation.
- Advanced SQL skills (complex joins, validation, performance-aware querying).
- Strong Python for analytics (reproducible analysis; comfortable with common DS tooling).
- Strong grounding in statistics and experimentation (hypothesis testing, confidence intervals, common pitfalls, bias/confounding; sample size/power concepts).
- Proven experience translating open-ended business questions into structured analyses that drive measurable outcomes.
- Demonstrated experience in customer analytics (behavioural analysis, funnels/journeys, cohorting, campaign measurement) and commercial thinking (profitability / unit economics).
- Ability to influence stakeholders and drive alignment in a fast-paced, agile, cross-functional environment in a fast-paced, agile environment, using clear analysis and communication.
- Excellent written and verbal communication skills in English.
Preferred qualifications, capabilities and skills
- Familiarity with quasi-experimental/causal approaches when randomisation isn’t feasible (e.g., diff-in-diff, matching/propensity approaches), and knowing when not to use them.
- Experience building interpretable predictive models (e.g., logistic/linear regression, tree-based methods) with disciplined evaluation (leakage checks, calibration, drift/monitoring considerations).
- Product and commercial curiosity: ability to balance rigor with speed and learn what is “good enough to decide” with guidance from senior partners.
