Staff Data Scientist - Product Analytics

About the RoleWe are looking for a Staff Data Scientist (Product Analytics) to build and lead product analytics end-to-end at Salla. In this role, you will own event instrumentation standards, tracking plans, and the measurement scaffolding that ensures every product launch, feature change, and checkout optimization is grounded in reliable data.You will establish Salla’s experimentation framework — defining how we run A/B tests, set guardrail metrics, conduct SRM checks, and think about statistical power and minimum detectable effects. Beyond methodology, you will partner closely with Product and Engineering to ensure measurement is baked into the development lifecycle, not bolted on after the fact.This role is ideal for someone who believes great product decisions start with great data, and who wants to build the analytical backbone of the largest e-commerce enabler in Saudi Arabia.ResponsibilitiesDefine and enforce event instrumentation standards and tracking plans across web, mobile, and backend systemsOwn core product funnel definitions (e.g., merchant onboarding, checkout, subscription conversion) and ensure they are accurately tracked, monitored, and understoodBuild and operationalise Salla’s experimentation framework, including:Experiment design guidance (metrics, guardrails, randomisation units)Statistical methodology (frequentist and/or Bayesian, sequential testing where appropriate)SRM checks, pre-experiment power analysis, and MDE calibrationPost-experiment synthesis and decision documentationEnsure trustworthy, reproducible reporting of experiment results — including clear communication of confidence levels, practical significance, and trade-offsPartner with Product Managers and Engineers to embed measurement into the product development lifecycle, from PRD through to post-launch reviewConduct deep-dive product analyses (retention drivers, feature adoption, user segmentation) that shape the product roadmapCollaborate with the Data Platform team on data quality, pipeline reliability, and self-serve analytics tooling for product teamsMentor product analysts and foster a culture of analytical rigour, intellectual honesty, and curiosityNice to HaveProficiency working in ArabicBased in Saudi (Jeddah, Makkah)Experience in e-commerce, marketplace, or SaaS product analyticsFamiliarity 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 approachesExperience working in the GCC7+ years of experience in product analytics, data science, or applied statistics at a technology companyDeep 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 reasoningExperience defining and maintaining product health metrics, guardrail metrics, and north-star metricsProven ability to synthesise experiment results and product data into coherent strategic narratives for product and engineering leadershipExcellent communication skills, with a track record of influencing product decisions through data#J-18808-Ljbffr