Founding Applied Data Scientist

Outtake•New York, NY

3d•Onsite

About The Position

Outtake is seeking a Founding Applied Data Scientist to establish and lead the company's data strategy. This role will be responsible for building the data foundation to inform product, business, and AI system performance. The ideal candidate will be adept at data pipelines, metric definition, SQL, and collaborating with various teams. This is a foundational role with the opportunity to shape the company's data infrastructure and influence key business decisions as Outtake scales.

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 Haves

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
Experience 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

Responsibilities

Own our analytical data pipeline and infrastructure across product, business, and AI performance data
Define and maintain the semantic layer in our product analytics stack, including Hex and the underlying warehouse models
Build canonical performance metrics for our product and business, including 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