Data Scientist — Causal Inference & Growth Analytics

Doist; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Clay is seeking a Data Scientist to join the Data Science and Analytics team. You will be durably embedded with a partner team—Product, Marketing, Sales, Finance, or Partnerships—as their data partner, shaping what gets built and how the business runs, supported by a central data team.

You will own ambitious problems end to end: experimental design, predictive modeling, and developing new metrics to guide business decisions.

Qualifications

• 5+ years in data science with ownership of experimentation, causal inference, or predictive modeling
• Strong product and business sense: connects analysis to decisions and revenue
• Strong statistical and experimentation foundations: design experiments, estimate effects, robust analytics
• Expert SQL and Python
• Experience using AI tools (Claude Code, cursor, or similar) to accelerate analytical work
• Clear communicator who can influence senior stakeholders

Responsibilities

• Own causal inference and experimentation
• Design and analyze experiments and build incrementality measurement where clean A/B tests aren’t possible
• Define how we measure the business and shape metric trees to connect team-level metrics to company outcomes
• Build predictive models that guide strategic focus and decision making
• Go deep on the toughest questions and investigate nuanced behavioral questions
• Partner as an embedded expert to leaders, turning questions into decision-ready analyses

Skills

Data Science

Experimentation

Causal Inference

SQL

Python

AI Tools

Communication

Tools

Snowflake

Github

dbt

Hex

Sigma

Dagster

Job description

Clay is seeking a Data Scientist to join the Data Science and Analytics team. You will be durably embedded with a partner team—Product, Marketing, Sales, Finance, or Partnerships—as their data partner, shaping what gets built and how the business runs, supported by a central data team.

You will own ambitious problems end to end: experimental design, predictive modeling, and developing new metrics to guide business decisions.