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About
Our team comes from high-performing engineering cultures, including Meta, Perplexity, AWS, Affirm, and leading AI research labs, including Princeton, Caltech, and Vector Institute.
We're looking for a Product Data Scientist who can dig into user behavior and product performance, run experiments, and build dashboards that reveal how customers use our web, API, and ML-powered products. You'll help teams make smarter product decisions, improve engagement and retention, and ensure our models deliver real value in production.
What you'll do:
Analyze user behavior across web and API products to uncover insights and growth opportunities Define, own, and evolve core product metrics (e.g. activation, engagement, retention, accuracy) Design and analyze experiments to evaluate product changes and inform decisions Build dashboards and selfserve analytics to support product and business decisions What we're looking for:
3+ years of experience in product data science, analytics, or a related role Very proficient with Python, SQL, and Pandas Strong foundation in statistics, experimentation, and causal inference Deep understanding of AI-powered products, including model performance, cost and speed tradeoffs Ability to translate business and product questions into rigorous analyses High ownership mindset with a bias toward action, learning, and iteration Comfort working in fastmoving, ambiguous, 0 → 1 environments Bonus Experience working closely with ML or research teams Experience with Databricks (PySpark) and Amplitude
Who you'll be joining
We're a small team. We value ownership, transparency, and listening to each other.
Everyone works and interacts with everyone. Everyone is free to attend meetings across product functions, whether it's diving into designs or dropping into our ML learning groups.
Here are some people you'll work closely with. You'll be meeting everyone. Edward (CEO), data-powered investigative journalism at BBC and Bellingcat, threat intelligence at Microsoft, ML researcher at Princeton Alex (CTO), Ph.D. dropout, R&D at Uber self-driving and Facebook, 3 patents in ML, 2021 and 2019 Best ML Hack at Stanford Hriday (Growth Lead), expert on taking products viral. Founded Wildfire, raised $3M, scaled it to 1M users and exited to Opendoor. Most recently, he worked on growth at Perplexity. Jacob (Full Stack), top of class at Waterloo, ex-Affirm, Lime, Level and Zynga Midhun (Full Stack), former India lead at Angelist, former co-founder of Nuance, Ex-Intuit/Bloomberg Our Perks
Health, dental, and vision coverage
Hybrid work in Downtown Office with lunch
Competitive salary
Competitive equity for a founding team member We are a cash-flow positive/profitable company experiencing exponential growth in multiple industries. We are open to sharing our growth metrics with applicants. Quarterly team retreats and offsites
Flexible PTO
Learning stipend, mentorship, and time with world-class advisors, including:
Tom Glocer (former CEO of Reuters, who recently reviewed the beta and is advising our product team on launching hallucination detection) Gaurav Vohra (former Head of Growth at Superhuman, taking them from $0 to $10Ms of revenue, advising our growth team biweekly) Russ Heddleston (exited CEO Docsend to Dropbox, recently dropped into all-hands to share additional strategies for growing our self-serve GTM motion) Ruslan Salakhutdinov (former director of AI at Apple, and current VP of Research for LLama, who meets monthly with our team to advise on AI model development) Amy Saper (founding product marketer at Stripe, who offers time with our team on growth strategies) Jack Altman (CEO of Lattice, brother of Sam Altman) on building great product culture Mike Smith (COO Walmart.com, COO StitchFix) and Jeff Barrett CTO StitchFix, on scaling a great engineering team.
At GPTZero, our recruiting team is involved in every step of the hiring process. We use AI-based tools (such as Endorsed.ai and Juicebox.ai) to help us to accelerate candidates at the resume review stage by marking when candidates met certain key criteria.
These tools are never the final say in a hiring decision - humans are.
Languages
- English
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