Staff Data Scientist - Business Analytics Salla

About the Role

We are looking for a Staff Data Scientist (Business Analytics) to lead strategic analytics and causal measurement for Salla s highest-impact growth bets. In this role, you will build the analytical frameworks that help leaders forecast outcomes, size opportunities, measure ROI, and understand true incrementality across the business.

You will develop decision-grade metrics and segmentation models, and partner with senior leadership to ensure capital and effort flow to the highest-return initiatives. This is a senior individual-contributor role for someone who combines deep statistical craft with strong business instinct and clear communication.

This role is ideal for someone who wants to shape how a pre-IPO e-commerce platform thinks about growth moving the company from intuition-driven bets to evidence-driven strategy.

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