Founding Applied Data Scientist

Outtake is an agentic AI platform that detects, monitors, and takes down impersonators to protect trust in the digital-first world. The Founding Applied Data Scientist will build the data foundation, analytical infrastructure, semantic layer, and performance metrics used to improve Outtake’s product, business, and AI systems. The role will also influence pricing, product strategy, AI evaluation, and the future growth of the data function.

Responsibilities

• 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

Skills

• 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

Benefits

• 100% company-paid medical, dental, and vision for employees.
• Flexible PTO.
• Annual company retreats and regular in-person events.

Company Overview

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