Data Scientist, Handshake AI

Handshake•San Francisco, CA

3d•$135,000 - $180,000•Remote

About The Position

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 related to expert performance, task quality, throughput, and overall operational efficiency. This is a hands-on role with real ownership, close partnership with product and operations, and a steep growth curve for someone who executes well.

Requirements

3–5 years of experience in Data Science, Product Analytics, Business Intelligence, or a related analytical role.
Expert-level SQL skills and experience working with large, complex datasets.
Strong analytical judgment and comfort working with imperfect, real-world data.
Experience defining metrics and using data to influence product or business decisions.
High ownership mindset and ability to independently drive complex work forward.
Strong communication skills and comfort partnering cross-functionally in a fast-paced environment.

Nice To Haves

Python experience for data analysis, such as pandas and notebooks.
Experience supporting operational, marketplace, quality, or workforce-related analytics.
Experience with experimentation, A/B testing, or metrics-driven product development.
Exposure to AI data workflows, data labeling, or evaluation processes.
Experience with analytics engineering, including building and maintaining DBT models.

Responsibilities

Write advanced SQL to analyze large, complex datasets related to expert performance, task quality, and operational workflows.
Define, build, and maintain core metrics that measure data quality, throughput, productivity, and operational efficiency.
Partner with Product, Operations, and cross-functional stakeholders to answer high-impact business and product questions.
Conduct analyses that inform workflow design, task improvements, and operational decision-making.
Build dashboards, reporting, and self-serve analytics that enable fast, informed decisions.
Identify data gaps, inconsistencies, and quality issues, then partner with relevant teams to resolve them.
Translate ambiguous business questions into clear analyses and actionable recommendations.
Communicate findings and recommendations clearly to both technical and non-technical stakeholders.