Staff GTM Data Scientist
Senior data scientist leading experimentation roadmap, causal inference, and advanced analytics to drive product and business impact. Partners cross-functionally to foster data-driven culture; requires 6+ years experience and expertise in Python/SQL/causal methods.
The Opportunity
As a Staff Data Scientist, you will serve as a senior analytical leader, embedding yourself deeply in product and business data to uncover insights and drive recommendations. You will champion a data-driven culture, advance experimentation capabilities, train analysts on causal methodologies, and provide leadership with clear impact understanding. You will report to the Director of GTM Data and partner with Go-to-Market, Marketing, Product, Finance, Design, Engineering, and executives.
What You'll Do
Experimentation & Causal Strategy
- Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact on customer workflows, churn risk, and LTV.
- Advanced Experiment Design: Design, implement, and analyze A/B tests, multivariate experiments, and Bayesian experimentation.
- Causal Inference Beyond A/B: Apply techniques like difference-in-differences, synthetic control, propensity score matching, and instrumental variables.
- Deep Dive Analysis: Conduct exploratory analysis to discover user behavior, trends, and root causes.
- Develop Measurement Frameworks: Define, instrument, and govern KPI frameworks mapping metrics to business outcomes.
Technical Leadership & Influence
- Scaling Data Science: Partner with Data Engineering to build scalable experimentation tooling and reusable assets like causal ML models.
- Strategic Influence: Translate statistical findings into actionable narratives for cross-functional partners and leadership.
- Mentorship and Training: Train junior and mid-level data scientists on statistical rigor, experimental design, and causal modeling.
About You
Qualifications
- Experience: 6+ years in applied data science, economics, or product analytics with proven impact via experimentation and causal inference.
- Education: B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or related. Master’s in quantitative field preferred.
Required Technical Expertise
- Causal Inference: Expertise in quasi-experimentation, PSM, difference-in-differences, instrumental variables.
- Experimentation Methodologies: Advanced A/B testing, sample size calculations, sequential testing, CUPED.
- Deep Analytical Methods: Mastery of statistical modeling, time-series analysis, EDA.
- Programming: Advanced Python or R (SciKit-Learn, numpy, pandas).
- Data Tools: Expert SQL, data warehouses (Snowflake, Postgres).
- Data Pipelining: dbt, Airflow, Databricks (strong plus).
Key Attributes
- Exceptional communication and influence skills.
- Proven change management in data-driven adoption.
- Thrive in ambiguity, manage priorities.
- SaaS and Product Data Science experience preferred.
Benefits
Annual base salary up to $190,000-$210,000. Includes career growth, health/commuter benefits, life/disability insurance, 20+ PTO days, 401K, FSA.
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