Staff Data Scientist – Product Analytics
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Job Description
Staff Data Scientist leading product analytics at Salla, the largest e-commerce enabler in Saudi Arabia. Responsible for event tracking, A/B testing, and collaboration with product teams.
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
Requirements:
- 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.
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