Senior Data Scientist - Generative AI
$221,380–$263,670 year
On-site · San Mateo, California, United States
Job Summary
Senior Data Scientist - Generative AI at Roblox drives evaluation, experimentation, and measurement frameworks for GenAI features and AI-assisted coding tools within the Foundation AI team. You will design and run online experiments (A/B tests), develop evaluation frameworks for GenAI features (text, image, video, 3D, 4D) or AI agents (Code Review, Refactor, Test Gen), define success metrics in areas like safety and developer productivity, and build automated evaluation tooling and dashboards. Key responsibilities include building metrics and analytics infrastructure, conducting rigorous experiments, defining leading/lagging indicators, and translating complex data into clear recommendations for leadership. Required qualifications include an advanced degree (PhD or Master’s), 5+ years in data science/analytics, strong SQL (Hive/Spark) and Python or R skills, solid grounding in experimentation and causal inference, problem-solving ability, learning agility, GenAI familiarity, and experience with engineering workflows. The position is based at Roblox headquarters in San Mateo, CA, with a base salary range of $221,380–$263,670 USD and full-time employment with equity opportunities. The company notes potential visa-related constraints for US work authorization and H-1B sponsorship. Roblox emphasizes equal employment opportunity and onsite office presence on select days.
Required Qualifications
- Advanced Degree: PhD or Master’s in Statistics, Economics, Computer Science, Applied Math, Physics, Engineering, or a related quantitative field
- 5+ years of experience in data science, analytics, or a quantitative role
- Strong proficiency in SQL (Hive/Spark) for manipulating large datasets and scripting languages (Python or R) for analysis and modeling
- Experimentation and causal inference expertise including test design and metric design for feature impact
- Problem solving and ability to frame ambiguous problems and deliver analytically grounded solutions
- Learning agility and ability to use AI tools to enhance productivity
- GenAI familiarity and knowledge of safety/quality evaluation methods; model training lifecycle is a plus (e.g., fine-tuning, RLHF, synthetic data generation)
- Experience with engineering development workflows and engineering efficiency data is a plus
- Applied research background; publications valued
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