Data Scientist
- Use advanced analytical and statistical methods to understand user behavior, product performance, and the impact of new features
- Design, run, and interpret A/B tests, incrementality experiments, and quasi-experimental analyses
- Define and evolve core product metrics that track engagement, health, value creation, and long-term success
- Build predictive or machine-learning models to personalize experiences, forecast demand, or reveal patterns that unlock new opportunities
- Partner with Product, Design, Engineering, Data, Commercial, and Operations to shape product strategy with rigorous insight
- Work with data engineering to ensure trustworthy instrumentation, scalable pipelines, and high-quality measurement foundations
- Develop reusable frameworks, measurement playbooks, and experimentation standards that elevate analytical maturity across teams
- Communicate insights through clear stories, visualizations, and recommendations that guide priorities and product decisions
- Advocate for thoughtful, ethical, and high-quality use of data across the product organization
- 4+ years of experience applying data science techniques in a product or digital environment
- Education: Bachelor’s or Master’s degree in Statistics, Computer Science, Data Science, Applied Math, Economics, Engineering, or a related quantitative field
- Strong proficiency with SQL and Python (or R) and experience working with large, complex datasets
- Hands-on experience designing, executing, and analyzing A/B tests and incrementality experiments
- Strong foundation in statistics, causal inference, modeling, and data exploration
- Experience with modern analytics or experimentation tools such as Snowflake, Databricks, Amplitude, Looker, or Optimizely
- Ability to distill complex analyses into clear insights and recommendations
- Proven success influencing decisions in cross-functional environments
- Experience working with multimodal or behavioral datasets
- Experience deploying predictive models into production or working within modern ML environments
- Familiarity with LLMs, generative AI, or agentic systems
- Experience in B2B or multi-stakeholder environments
- Comfort with data visualization tools such as Looker or Tableau
- Analytical rigor: Applies strong statistical and causal methods to break down ambiguous problems and uncover meaningful drivers, patterns, and opportunities.
- Product thinking: Connects modeling and experimentation to real user behavior, value creation, and product strategy; influences direction through clarity and evidence.
- Experimentation and measurement: Designs robust KPIs, constructs reliable measurement frameworks, and leads A/B and quasi-experimental analyses that reduce uncertainty and accelerate learning.
- Modeling expertise: Builds interpretable, scalable models that inform decisions, enable personalization, forecast behavior, or reveal new opportunities.
- Data storytelling: Synthesizes complex analyses into clear narratives and visualizations that influence priorities and align cross-functional teams.
- Collaboration and influence: Works deeply within product teams, elevates analytical standards, and helps teams adopt evidence-based decision-making.
- Bias for impact: Balances rigor with speed; focuses on insights and models that meaningfully shape decisions and outcomes.
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