Data Scientist Associate

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Skills you'll use

What you'll do

• 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.

Who can apply

• 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.

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