What are the responsibilities and job description for the Sr. Data Scientist, GenAI & Labeling Platforms position at ChatGPT Jobs?
Job Description
Job Information Summary
Job Description Summary
Job Title
Data Scientist (Senior Individual Contributor)
Company
Pinterest
Location & Work Model
This role focuses on advancing the science and systems behind data labeling, evaluation, and Generative AI (GenAI)-enabled workflows. The position involves driving step-function improvements in data labeling capabilities by leveraging Large Language Models (LLMs), developing human-in-the-loop quality systems, and optimizing prompt/rubric design. The successful candidate will act as a senior individual contributor, executing high-impact technical work and partnering cross-functionally to turn ideas into durable platform capabilities.
Key Responsibilities
Essential Experience
Job Information Summary
Job Description Summary
Job Title
Data Scientist (Senior Individual Contributor)
Company
Location & Work Model
- Primary Location: San Francisco, CA
- Work Style: Hybrid (On-site & Remote)
- Remote Eligibility: Open to candidates situated anywhere in the country.
- In-Office Requirement: 1-2 times per quarter for in-person collaboration.
- Relocation: Not eligible for relocation assistance.
This role focuses on advancing the science and systems behind data labeling, evaluation, and Generative AI (GenAI)-enabled workflows. The position involves driving step-function improvements in data labeling capabilities by leveraging Large Language Models (LLMs), developing human-in-the-loop quality systems, and optimizing prompt/rubric design. The successful candidate will act as a senior individual contributor, executing high-impact technical work and partnering cross-functionally to turn ideas into durable platform capabilities.
Key Responsibilities
- Execute high-impact scientific work across GenAI-powered labeling and evaluation systems.
- Identify opportunities where LLMs can improve quality, speed, coverage, and cost efficiency.
- Develop prototypes demonstrating value in prompt optimization, task decomposition, quality estimation, routing, and human-in-the-loop workflows.
- Design experiments and measurement frameworks to evaluate model performance and operational tradeoffs.
- Partner with engineering, product, and data science teams to productionize successful approaches.
- Apply standards for trustworthiness (bias measurement, calibration, quality control, responsible oversight).
- Contribute to reusable methods and frameworks scalable across teams.
- Support junior scientists and contribute to the technical health of the team.
Essential Experience
- Education/Experience: 6 years of combined post-graduate academic and industry experience (or PhD 3 years) applying scientific methods to real-world problems on large-scale data.
- Technical Skills: Strong hands-on experience as an individual contributor solving technically complex, high-impact data science or ML problems.
- GenAI Expertise: Experience applying LLMs or other generative AI techniques to practical workflows, systems, or products.
- Problem Solving: Ability to turn ambiguous problems into rigorous analyses, experiments, and prototypes.
- Engineering: Track record of writing high-quality code and using technical work to influence product or platform direction.
- Collaboration: Solid cross-functional collaboration skills and business/product sense to define meaningful success metrics.
- Adaptability: Self-directed learning mindset comfortable in a rapidly evolving technical landscape.
- Experience with labeling systems, evaluation frameworks, human judgment workflows, or internal AI tooling.