Data Science Lead Engineering Manager

Primary: Bay Area (San Francisco / Peninsula)   |   Secondary: NYC

The Opportunity

We're doing an AI-first engineering rebuild for a company that already has an audience of 100M+ people. This is a zero-to-one build with no legacy constraints. You're here to build a data foundation: establishing AI-facilitated analysis processes and tooling that unlock super-fast feature iteration. Alongside that, you'll architect the large-scale data pipeline that supports real-time telemetry, long-term metric monitoring, and A/B experimentation at scale.

The Product

You'll be the senior technical anchor for data science, owning the full journey from business question to data analysis to product insight that drives decisions. The work spans consumer products, media, game, and e-commerce analytics, all sitting on top of a large and fast-growing user base. That means:

• Build the data analysis mechanisms that turn raw user behavior into actionable product insights, powering decisions on what to build, what to fix, and where the biggest opportunities are.
• Architect and own the data platform end to end: the full tech stack covering data collection, processing, and visualization that makes reliable, real-time analytics possible at scale.
• Leverage AI to accelerate the productivity of the data science team, embedding AI-facilitated tooling and workflows that enable faster product iteration and shorter feedback loops.

What You'll Do

• Set and enforce the data standards for product launches: validation, reproducibility, experimentation, and monitoring frameworks that every team relies on before and after shipping.
• Own the A/B experimentation framework end to end, from experiment design and statistical rigor through to result interpretation and product decision-making.
• Own the engineering and operation of the data pipeline, ensuring the right data freshness, coverage, and observability to support product and business decisions at scale.
• Guide the technical work of data scientists and engineers through design review, pairing, and mentorship, growing the team's capabilities alongside the product.

Who You Are

• AI-Native: You're already burning through tokens and using AI tools daily to accelerate your data science workflow, from analysis to insight to decision.
• Data Science Builder: 8+ years designing, building, and operating data science systems in production, with deep expertise in statistical modeling, experimentation design, and data pipeline engineering.
• End-to-End Owner: You've owned the full journey from business question through data collection, analysis, and insight delivery, and you're accountable for the quality and impact of the output, not just the methodology.
• Honest Communicator: You frame problems as testable hypotheses, hold the line on validation rigor under deadline pressure, and communicate uncertainty honestly instead of overselling.

Strong data science practice: reproducible pipelines, rigorous experimentation frameworks, production-quality code, and a track record of turning data into decisions that measurably improved product or engineering quality. Bonus points for experience with AI-assisted data science tools and processes, and domain exposure in consumer products, media, or gaming.

Benefits

• Equity: Highly competitive equity package designed for a foundational hire.
• Hybrid Model: Expected ~3 days per week in-office (Bay Area or NYC).