Founding Applied Data Scientist

About the role

We're seeking a Founding Applied Data Scientist to define how Outtake understands, measures, and improves its product, business, and AI systems. This role involves building the data foundation that transforms messy data into clear decisions and durable systems. Key responsibilities include:

• Own our analytical data pipeline and infrastructure across product, business, and AI performance data
• Build the semantic layer in our product analytics stack, including Hex and the underlying warehouse models
• Define canonical performance metrics for our product and business, such as activation, usage, retention, customer value, operational efficiency, and agent effectiveness
• Partner with Product and Finance to refine pricing models, usage-based packaging, margin analysis, and customer-level profitability
• Work with Platform Engineering on internal AI performance metrics, evals, benchmarking, observability, and reliability reporting
• Design dashboards, analyses, and decision-support systems that help the team make fast, high-confidence product and business decisions
• Build data quality checks, documentation, and metric definitions that make our data trustworthy and easy for others to use
• Translate ambiguous questions from product, GTM, finance, and engineering into rigorous analyses and practical recommendations
• Hire and onboard Outtake’s Data Team and set the long-term roadmap for data infrastructure, analytics, and applied data science

Requirements

• 5+ years of combined experience as an Engineer, Analytics Engineer, Data Scientist, or closely related role
• Strong proficiency in SQL, ideally with production experience in Postgres and modern analytical modeling patterns
• Experience building reliable data pipelines, data models, semantic layers, or internal analytics infrastructure
• Experience defining product and business metrics from first principles, not just reporting on pre-existing dashboards
• Experience working with modern AI eval frameworks, model performance measurement, LLM observability, or similar systems
• Strong product judgment and the ability to turn ambiguous business or product questions into clear analytical approaches
• Comfort working cross-functionally with Product, Engineering, Finance, and GTM stakeholders
• Ability to communicate complex analyses clearly, including the tradeoffs, caveats, and recommendations that matter
• High ownership, strong bias toward action, and comfort operating in a fast-moving, early-stage environment
• Desire to build foundational systems and eventually help hire, mentor, and scale a high-performing data function

Nice to have

• Experience working with Hex or similar collaborative analytics tools
• Experience working with Langfuse, Braintrust, Arize, Phoenix, OpenTelemetry, or similar AI observability/eval tooling
• Experience working with ClickHouse, BigQuery, Snowflake, Databricks, DuckDB, dbt, or similar analytical data systems
• Experience with usage-based pricing, unit economics, margin modeling, or customer-level profitability analysis
• Experience building metrics or evals for AI agents, LLM products, fraud systems, abuse detection, cybersecurity, or trust & safety products
• Experience designing experimentation, causal inference, forecasting, or decision science workflows
• Experience building internal tools, notebooks, or lightweight apps that help non-data teammates answer their own questions
• As an early data hire or founding team member in a high-growth startup
• Comfort writing production-quality Python or TypeScript when needed
• Prior startup experience or a track record of thriving in high-ownership environments