Staff Data Scientist - Product Analytics

Responsibilities

• Define and enforce event instrumentation standards and tracking plans across web, mobile, and backend systems
• Own core product funnel definitions (e.g., merchant onboarding, checkout, subscription conversion) and ensure they are accurately tracked, monitored, and understood
• Build 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 calibration
• Post-experiment synthesis and decision documentation
• Ensure trustworthy, reproducible reporting of experiment results including clear communication of confidence levels, practical significance, and trade-offs
• Partner with Product Managers and Engineers to embed measurement into the product development lifecycle, from PRD through to post-launch review
• Conduct deep-dive product analyses (retention drivers, feature adoption, user segmentation) that shape the product roadmap
• Collaborate with the Data Platform team on data quality, pipeline reliability, and self-serve analytics tooling for product teams
• Mentor product analysts and foster a culture of analytical rigour, intellectual honesty, and curiosity
• Nice to Have
• Proficiency working in Arabic
• Based in Saudi (Jeddah, Makkah)
• 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
• Experience working in the GCC
• 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