Data Scientist III, Product Analytics

Role Overview

• 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.

Tech Stack

• Python
• SQL
• Tableau

Benefits

• Full benefits package
• Exciting travel perks
• Generous time-off
• Parental leave
• Flexible work model
• Career development resources