{"id":27085,"url":"https://alion.io/job/outtake-founding-applied-data-scientist","title":"Founding Applied Data Scientist","company":{"id":7293,"name":"Outtake","domain":"outtake.com","url":"https://alion.io/company/outtake","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":5,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":196,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":146000,"max_usd":317000,"period":"year","method":"role_country_seniority_unknown","sample_n":3203},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"LLM","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"SQL","optional":false},{"name":"Arize Phoenix","optional":true},{"name":"BigQuery","optional":true},{"name":"Braintrust","optional":true},{"name":"ClickHouse","optional":true},{"name":"Databricks","optional":true},{"name":"dbt","optional":true},{"name":"DuckDB","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Langfuse","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-07-28T18:39:09Z","employer_posted_date":"2026-07-28","last_verified_at":"2026-10-17T17:19:54Z","board_verified":true,"closed_at":null,"days_open":80,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":80},"description":"About Outtake\nOuttake 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.\nBuilt 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.\nOuttake 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.\nThe Role\nThis 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.\nOur 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.\nThis 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.\nWhat You’ll Do\nOwn our analytical data pipeline and infrastructure across product, business, and AI performance data.\n\nBuild and own the long-term roadmap for data infrastructure, analytics, and applied data science at Outtake.\n\nDefine and maintain the semantic layer in our product analytics stack, including Hex and the underlying warehouse models.\n\nBuild canonical performance metrics for our product and business, including activation, usage, retention, customer value, operational efficiency, and agent effectiveness.\n\nPartner with Product and Finance to refine pricing models, usage-based packaging, margin analysis, and customer-level profitability.\n\nWork with Platform Engineering on internal AI performance metrics, evals, benchmarking, observability, and reliability reporting.\n\nDesign dashboards, analyses, and decision-support systems that help the team make fast, high-confidence product and business decisions.\n\nBuild data quality checks, documentation, and metric definitions that make our data trustworthy and easy for others to use.\n\nTranslate ambiguous questions from product, GTM, finance, and engineering into rigorous analyses and practical recommendations.\n\nAs the data function matures, we expect to grow this into a broader team with dedicated ownership across analytics engineering, AI evaluation, and business intelligence.\n\nWhat We’re Looking For\n5+ years of combined experience as an Engineer, Analytics Engineer, Data Scientist, or closely related role.\n\nStrong proficiency in SQL, ideally with production experience in Postgres and modern analytical modeling patterns.\n\nExperience building reliable data pipelines, data models, semantic layers, or internal analytics infrastructure.\n\nExperience defining product and business metrics from first principles, not just reporting on pre-existing dashboards.\n\nExperience working with modern AI eval frameworks, model performance measurement, LLM observability, or similar systems.\n\nStrong product judgment and the ability to turn ambiguous business or product questions into clear analytical approaches.\n\nComfort working cross-functionally with Product, Engineering, Finance, and GTM stakeholders.\n\nThe ability to communicate complex analyses clearly, including the tradeoffs, caveats, and recommendations that matter.\n\nHigh ownership, a strong bias toward action, and comfort operating in a fast-moving, early-stage environment.\n\nNice to have: experience working with Hex or similar collaborative analytics tools.\n\nNice to have: experience as an early data hire or founding team member in a high-growth startup.\n\nNice to have: experience working with Langfuse, Braintrust, Arize, Phoenix, OpenTelemetry, or similar AI observability/eval tooling.\n\nNice to have: experience working with ClickHouse, BigQuery, Snowflake, Databricks, DuckDB, dbt, or similar analytical data systems.\n\nNice to have: experience with usage-based pricing, unit economics, margin modeling, or customer-level profitability analysis.\n\nNice to have: experience building metrics or evals for AI agents, LLM products, fraud systems, abuse detection, cybersecurity, or trust & safety products.\n\nNice to have: experience building internal tools, notebooks, or lightweight apps that help non-data teammates answer their own questions.\n\nLife at Outtake\nOffice: 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.\n\nHealth: 100% company-paid medical, dental, and vision for employees.\n\nTime Away: Flexible PTO. We trust adults to manage energy, not clock time.\n\nCulture & Team: Annual company retreats and regular in-person events.\n\nOuttake is an equal opportunity employer. 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