Data Scientist, Handshake AI

Handshake•San Francisco, CA

About The Position

Handshake is building the career network for the AI economy. Our three-sided marketplace connects 18 million students and alumni, 1,500+ academic institutions across the U.S. and Europe, and 1 million employers to power how the next generation explores careers, builds skills, and gets hired. Handshake AI is a human data labeling business that leverages the scale of the largest early career network. We work directly with the world’s leading AI research labs to build a new generation of human data products. From PhDs in physics to undergrads fluent in LLMs, Handshake AI is the trusted partner for domain-specific data and evaluation at scale. This is a unique opportunity to join a fast-growing team shaping the future of AI through better data, better tools, and better systems—for experts, by experts. We’re looking for a highly motivated Data Scientist with strong SQL expertise to support analytics, measurement, and operational insights across our human data training workflows. You’ll work with messy, fast-moving data tied to expert performance, task quality, throughput, and model outcomes. This is a hands-on role with real ownership, exposure to frontier AI work, and a steep growth curve for people who execute well.

Requirements

3–5 years of professional experience as a Data Scientist, with a strong track record of applying data to drive product or business decisions.
Expert-level SQL skills
Python experience for data analysis (e.g., pandas, notebooks).
Strong analytical instincts and comfort working with imperfect, real-world data.
High ownership mindset and willingness to grind through complex problems.
Ability to operate independently and deliver under tight timelines.
Clear communication and comfort working cross-functionally.

Nice To Haves

Experience with experimentation or metrics-driven development.
Exposure to AI/ML workflows, data labeling, or model evaluation.

Responsibilities

Write advanced SQL to analyze large, complex datasets related to expert performance, task quality, and AI training workflows.
Build and maintain core metrics that track data quality, throughput, and operational efficiency.
Partner with product, operations, and AI stakeholders to answer high-impact questions.
Support experimentation and analysis that improves task design, data quality, and model outcomes.
Create dashboards and reporting that drive fast, informed decision-making.
Identify data gaps or quality issues and work cross-functionally to resolve them.
Take ambiguous problems and drive them to clear, actionable conclusions.