Partner with product teams to clarify business problems, define success metrics, and translate ambiguous questions into structured analytical plans
Design, run, and analyze experiments (A/B tests, pre/post, quasi-experimental reads) to measure impact, quantify trade-offs, and guide iteration
Perform deep-dive product analysis (funnels, cohorts, segmentation, sizing) to uncover drivers of performance and identify opportunities for growth and customer experience improvements
Build reliable datasets and dashboards using SQL and modern BI tools, enabling self-serve product performance monitoring across platforms (web, app, partner)
Apply solid statistical thinking (probability, sampling, inference, regression) to ensure reads are robust, distinguish signal from noise, and clearly communicate caveats
Use Python/R (or similar) to prototype models and advanced analyses when needed (e.g., regression, clustering, simple prediction) and to automate recurring analytics workflows
Tell clear, compelling stories that move stakeholders from insight to action—framing context, methods, results, and recommendations for both technical and non-technical audiences
Champion data quality and reproducibility by following best practices for data validation, query performance, version control, and documentation
Collaborate and upskill others by seeking peer review, sharing best practices, and providing light mentorship to more junior analysts or data scientists
Requirements
5+ years of experience in analytics, product analytics, or data science roles
Bachelor’s or Master’s degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Economics, Engineering) or equivalent practical experience
Proven track record of delivering data-driven insights and recommendations that influenced product decisions or drove measurable performance improvements
Strong SQL: able to work confidently with large, complex datasets; write intermediate queries (joins, subqueries, CASE logic, window functions, unions); and optimize for performance and cost
Experience with at least one scripting language such as Python or R for analysis, modeling, and automating recurring tasks
Comfortable with hypothesis testing, confidence intervals, experiment design, and interpreting regression/logistic regression outputs
Hands-on experience with A/B testing platforms and understanding of when to use experiments vs. observational or exploratory analysis
Data visualization and dashboarding skills (e.g., Tableau, Power BI, or similar) with a focus on clarity, appropriate chart selection, and inclusive design basics (e.g., color use, accessibility)
Ability to build and validate basic models (e.g., linear/logistic regression, simple clustering) and understand data/feature requirements and key assumptions
Demonstrated product sense: ability to connect metrics to customer journeys, refine problem statements, and propose pragmatic analytical approaches aligned with business timelines
Experience defining or refining product KPIs, building scorecards, and monitoring performance for ongoing features and launches
Strong critical thinking and problem-solving skills; able to break complex problems into manageable analytical steps and iterate based on learnings
Excellent communication and influencing skills—comfortable presenting to PMs, engineers, designers, and senior leaders, and adapting depth/rigor to the audience
Collaborative working style, with a proactive, ownership-oriented mindset and openness to feedback, peer review, and continuous learning.