Founding Applied Data Scientist

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.

About the role

We’re looking for a Founding Applied Data Scientist to define how Outtake understands, measures, and improves the performance of our product, business, and AI systems. At a high level, this person will build 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. We need to know what is working, where our agents are succeeding or failing, how customers are experiencing value, and how the business should price, package, and prioritize our work. Data is how we make those questions legible.

This role is for someone who is equally comfortable building pipelines, defining metrics, writing SQL, partnering with product and finance, and reasoning about AI evals. You should be excited to operate at the intersection of analytics engineering, applied data science, product strategy, and AI systems evaluation.

We are early, so 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.

What you’ll do

• 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

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

• 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

Life at Outtake

• Office: For those in NY, we are an in-person team (5 days a week, with flexibility as needed) working out of a stunning waterfront office in Brooklyn. Collaboration, speed, and clarity matter.

• Health: 100% company-paid medical, dental, and vision for employees.

• Time Away: Flexible PTO. We trust adults to manage energy, not clock time.

• Culture & Team: Annual company retreats and regular in-person events.

Outtake is an equal opportunity employer. We are committed to building a diverse, inclusive team.