About Outtake

Outtake exists to empower and facilitate trust for a digital-first world. Today, impersonation, fraud, AI-driven scams, and identity abuse spread faster than any security team can respond to. So we built something different: an agentic AI platform that proactively detects, monitors, and takes down impersonators, automating protection in hours, not weeks.

Built by ex-Palantir, ex-CTO/founders, and ex-Notion engineers, Outtake is designed for clarity, autonomy, and velocity. Our goal is ambitious: become the trust layer of the modern internet. And we intend to do it with a lean, dense, exceptionally talented team.

Outtake is backed by top-tier investors and operators who believe in our mission and our model. With strong financial footing and a long runway, we prioritize creating an environment where people can do the best work of their careers.

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.