Staff Data Scientist - Business Analytics

About the Role

Responsibilities

• Design and maintain forecasting frameworks for revenue, GMV, merchant growth, and other key business drivers
• Lead opportunity sizing for new products, pricing changes, market expansions, and strategic initiatives
• Build causal impact measurement capabilities (e.g., synthetic control, diff-in-diff, geo-experiments) to quantify the true ROI of business investments
• Develop and maintain a repeatable measurement playbook that standardises how the company evaluates bets covering incrementality, attribution, and counterfactual reasoning
• Create merchant and customer segmentation and scoring models that drive prioritisation across sales, marketing, and merchant success
• Define decision-grade metrics and KPIs in partnership with Finance, Commercial, and Product leadership
• Translate complex analytical findings into clear, actionable narratives for C-level and cross-functional stakeholders
• Mentor analysts and data scientists across the team on statistical methods, causal inference, and analytical rigour

Requirements

• Experience in e-commerce, marketplace, or SaaS product analytics
• Familiarity with experimentation platforms (e.g., Eppo, Statsig, LaunchDarkly, Optimizely, or internal tooling)
• Experience with BI tools (Looker, Metabase, or equivalent) and modern data stacks (dbt, ClickHouse, BigQuery)
• Knowledge of Bayesian methods, sequential testing, or multi-armed bandit approaches

Qualifications

• 7+ years of experience in product analytics, data science, or applied statistics at a technology company
• Deep hands-on experience designing, running, and analysing A/B tests at scale, including familiarity with common pitfalls (SRM, peeking, metric sensitivity, novelty effects)
• Strong understanding of event-tracking architectures and instrumentation best practices (e.g., event taxonomies, naming conventions, schema governance)
• Expert-level SQL and Python (pandas, scipy, statsmodels, or equivalent)
• Solid statistical foundation: hypothesis testing, confidence intervals, power analysis, multiple comparisons, and causal reasoning
• Experience defining and maintaining product health metrics, guardrail metrics, and north-star metrics
• Proven ability to synthesise experiment results and product data into coherent strategic narratives for product and engineering leadership
• Excellent communication skills, with a track record of influencing product decisions through data