Lead Data Scientist

Snorkel AI•San Francisco, CA

9d•$130,000 - $200,000

About The Position

Our Data & Analytics Platform team recently migrated from Redshift to Snowflake, enabled 100+ users, and centralized all business data in under three months. We are a lean team with a clear remit: ensure a single source of truth for all business data. With company-wide Snowflake adoption complete, we are hiring a Lead Data Scientist to expand our mandate to Generative AI Analytics and Snorkel’s highest-leverage data science problems. This is a senior individual contributor role with no direct reports; just ownership, autonomy, and impact from pioneering new insights and models and building our semantic layer, supply/demand matching algorithms, forecasting models, and other data science foundations that compound. How you'll allocate your time (estimates subject to change): 40%: Building and maintaining Generative AI Analytics 40%: Critical-path data science projects 20%: Partnering with Engineers, PMs, DaaS operational leads, and Finance

Requirements

5+ years: data science or related experience, with a track record of shipping forecasting, ranking, recommendation, or similar models into production.
SQL, Snowflake or a similar data warehouse, and Python experience.
Experience deploying modern AI tools (semantic layers, LLMs, evaluation frameworks) reliably into production.
Proven ability to partner with Engineering teams to bridge the gap from prototype to production system.
Strong track record of impact without a large team or detailed roadmap.
Comfortable building foundational systems from scratch in environments where data infrastructure is still maturing.
Genuinely values engaging technical and operational stakeholders to fully understand a problem and build data-driven solutions.

Nice To Haves

Experience with Streamlit, Snowflake Cortex, AI/data labeling, A/B testing, and two-sided marketplace or data product business models.

Responsibilities

Own Generative AI Analytics: Build and maintain end-to-end infra (semantic layer, LLM tooling, evals, and agents) to analytically empower every team at Snorkel.
Protect Quality: Work with Engineering and the Fraud Operations Lead to build, unify inputs for, and deploy fraud detection and contributor quality models.
Optimize Marketplace: Architect search, ranking, and recommendation models to match project needs for Expert Contributors across domains and geographies.
Improve Forecasting: Build predictive models to help Strategy & Operations and Finance elevate forecast accuracy with signals from across the business.
Identify Leverage: Surface the next high-value data science opportunities at Snorkel and build the case for their prioritization with the Head of Data.
Advise Leadership: Be a trusted thought partner to the Head of Data and leaders of other teams on data architecture, applied data science, and AI.