Founding Applied Data Scientist

The Role

This is a Founding Applied Data Scientist role. You will define how Outtake understands, measures, and improves the performance of our product, business, and AI systems, building the data foundation that turns messy product usage, customer outcomes, model behavior, and business operations into clear decisions and durable systems.

Our product operates in a noisy, adversarial, fast-moving threat landscape. You will make legible what is working, where our agents are succeeding or failing, how customers are experiencing value, and how the business should price, package, and prioritize its work. The role begins as an individual contributor position; the desire to eventually hire, mentor, and scale a high-performing data function will be valuable as the function grows.

This is not a narrow reporting or dashboarding role. You will shape the semantic layer, define canonical metrics, build the analytical infrastructure, influence product and pricing decisions, and set the long-term bar for how Outtake uses data as the company scales - operating comfortably at the intersection of analytics engineering, applied data science, product strategy, and AI systems evaluation.

What You’ll Do

• Own our analytical data pipeline and infrastructure across product, business, and AI performance data.
• Build and own the long-term roadmap for data infrastructure, analytics, and applied data science at Outtake.
• 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.
• As the data function matures, we expect to grow this into a broader team with dedicated ownership across analytics engineering, AI evaluation, and business intelligence.

What We’re Looking for

• 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.
• The ability to communicate complex analyses clearly, including the tradeoffs, caveats, and recommendations that matter.
• High ownership, a strong bias toward action, and comfort operating in a fast-moving, early-stage environment.
• Nice to have: experience working with Hex or similar collaborative analytics tools.
• Nice to have: experience as an early data hire or founding team member in a high-growth startup.
• Nice to have: experience working with Langfuse, Braintrust, Arize, Phoenix, OpenTelemetry, or similar AI observability/eval tooling.
• Nice to have: experience working with ClickHouse, BigQuery, Snowflake, Databricks, DuckDB, dbt, or similar analytical data systems.
• Nice to have: experience with usage-based pricing, unit economics, margin modeling, or customer-level profitability analysis.
• Nice to have: experience building metrics or evals for AI agents, LLM products, fraud systems, abuse detection, cybersecurity, or trust & safety products.
• Nice to have: experience building internal tools, notebooks, or lightweight apps that help non-data teammates answer their own questions.