The opportunity
You're an experienced data scientist who gets excited about understanding how customers interact with a product – and using that understanding to help teams make smarter decisions. You're a trusted data storyteller: you can write a tight SQL query, design a rigorous experiment,…
What you'll do
Partner with product leaders to define and measure goal metrics
Design and analyze A/B tests, quasi-experiments, and causal analyses to: measure the impact of product changes and help teams understand not just if something worked, but why
Accelerate product learning cycles through frameworks for experimentation,: measurement, and iteration so teams can move faster without sacrificing rigor
Analyze strategic opportunities to inform roadmap prioritization
Build compelling narratives from complex data, presenting findings in ways: that influence decisions with both technical and non-technical audiences, from product pods to senior leadership
Identify friction and opportunity across the customer lifecycle, from: onboarding flows to feature adoption to retention signals, and translate what you find into actionable strategies
What they're looking for
- + years of experience as a data scientist or analyst, with a track record of: driving business impact through statistical analysis, experimentation, and predictive modeling
- Strong expertise in experimental design, causal inference, and statistical: methods — you know how to draw defensible conclusions from imperfect data
- Excellent communication and data storytelling skills: you can translate complex findings into clear narratives that influence strategy and decisions at all levels of the organization
- Strong business acumen and the ability to connect analysis to product: strategy, roadmap tradeoffs, and business outcomes
- Fluent in SQL
- Proficient in Python for reproducible analysis, statistical modeling, experimentation, and automation
- Familiarity with a cloud-based data stack (e.g. Snowflake, dbt)
- Demonstrated curiosity about AI tools and emerging technologies, with a track: record of applying them to accelerate analysis, improve rigor, or expand the reach of data science work
