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Handshake AI is a rapidly growing division within Handshake focused on expert-labeled data collection at scale, connecting domain experts with leading AI companies to train frontier models. The company is positioned as a top-three player in the human data sector and aims to become the default platform for the AI data economy.
In this hands-on Data Scientist role, you will support analytics, measurement, and operational insights across human data training workflows. You'll own real analytical work with close partnership with product and operations teams, working with complex, fast-moving data related to expert performance, task quality, throughput, and operational efficiency.
Key responsibilities include:
- Writing advanced SQL to analyze large, complex datasets related to expert performance, task quality, and operational workflows
- Defining, building, and maintaining core metrics that measure data quality, throughput, productivity, and operational efficiency
- Partnering with Product, Operations, and cross-functional stakeholders to answer high-impact business and product questions
- Conducting analyses that inform workflow design, task improvements, and operational decision-making
- Building dashboards, reporting, and self-serve analytics to enable fast, informed decisions
- Identifying data gaps, inconsistencies, and quality issues, then partnering with relevant teams to resolve them
- Translating ambiguous business questions into clear analyses and actionable recommendations
- Communicating findings and recommendations clearly to both technical and non-technical stakeholders
The team includes leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir. You'll work with world-class AI labs, Fortune 500 partners, and top educational institutions.
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
Desired qualifications:
- Python experience for data analysis (pandas, 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 DBT models
