Data Scientist

Clay Labs•New York, NY

1d•$170,000 - $300,000

About The Position

Clay is seeking a Data Scientist to join its Data Science and Analytics team. This role involves being embedded within a partner team (Product, Marketing, Sales, Finance, or Partnerships) as their dedicated data partner. The position requires a deep understanding to influence product development and business operations, supported by a central data team acting as a center of excellence. This role is ideal for individuals who thrive on owning complex, ambiguous problems from inception to completion, including experiment design, predictive model building, and the creation of new metrics to guide business strategy. Data scientists at Clay are expected to be strategic leaders and trusted sources of truth. The company is a rapidly growing AI business, and the data science team is at the forefront of AI-enabled data science practices.

Requirements

5+ years in data science, with demonstrated ownership of experimentation, causal inference, or predictive modeling.
Strong product and business sense: ability to connect analysis to decisions and revenue, with a proven track record of influencing product and business decisions through analytical work.
Strong statistical and experimentation foundations: including experiment design, effect estimation, a comprehensive exploratory analysis toolkit, and the ability to discern when a result is unreliable.
Expert proficiency in SQL and Python.
Experience using AI tools (e.g., Claude Code, Cursor) to accelerate analytical work.
Clear communication skills with the ability to influence senior stakeholders.

Nice To Haves

Experience with the company's tech stack: Snowflake, Github, dbt, Hex, Sigma, Dagster.
Experience in product-led growth or B2B SaaS, particularly with usage-based pricing.
Experience partnering with ML engineers to productionize models.

Responsibilities

Own causal inference and experimentation, including designing and analyzing experiments and building incrementality measurement for situations where A/B tests are not feasible (e.g., using propensity-score matching to estimate the true lift of features and GTM channels).
Define how the business is measured by shaping metric trees that link team-level metrics to company outcomes, establishing success criteria for product launches, and building frameworks for impact sizing and forecasting.
Build predictive models to help the team prioritize the most important areas.
Investigate root causes of anomalies, explore nuanced behavioral questions, and develop new analysis frameworks for situations that don't fit existing patterns.
Use data to educate the entire company on key focus areas and areas to deprioritize.
Act as a direct analytical partner to leaders in their assigned area, transforming ambiguous strategic questions into rigorous, decision-ready analyses.