Data Scientist — Quality & Pipeline Intelligence

Role overview Develop data selection and quality systems that help teams identify the most valuable examples for model training and catch problems in complex datasets. The role combines applied machine learning, statistical validation, and collaboration with platform engineers to bring data checks into production.

Responsibilities - Build active learning methods that prioritize high-uncertainty data for human review. - Create anomaly detection tools to flag label drift, annotation noise, and departures from guidelines. - Produce statistical validation reports and performance scorecards for deployed work. - Partner with platform engineers to integrate data validation pipelines into production systems.

Requirements - Bachelor’s or master’s degree in data science, statistics, computer science, or a related quantitative field. - Hands-on Python experience and familiarity with PyTorch or scikit-learn. - Experience with statistical hypothesis testing. - Experience working with unstructured data such as images, audio, text, or LiDAR point clouds.

Nice to have - Background in machine learning data curation or active learning.

Benefits and work setup - Remote role based in the United States. - Salary range of $130,000–$165,000 plus equity. - Health, dental, and vision coverage, and annual in-person global retreats. - Work involving foundation models and autonomous systems.