Remote Data Scientist: Quality & Active Learning

Engai; United States; Remote; USD 130,000 - 165,000; Full time

Benefits offered by this job

Remote role in the United States

Equity

Health, dental, and vision

Annual in-person global retreats

Foundation models and autonomous data

Professional growth

Job summary

Engai is seeking a data scientist to develop data selection and quality systems that help teams identify 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.

You will build active learning methods and anomaly detection tools, produce statistical validation reports, and partner with platform engineers to integrate data

Qualifications

• 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.

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.

Skills

Active learning

Anomaly detection

Statistical validation

Production collaboration

Education

Bachelor’s or Master’s degree in data science, statistics, computer science, or related quantitative field

Tools

Python

PyTorch

scikit-learn

Job description

Engai is seeking a data scientist to develop data selection and quality systems that help teams identify 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.

You will build active learning methods and anomaly detection tools, produce statistical validation reports, and partner with platform engineers to integrate data