JPMorganChase

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. 

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. 

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.