Data Scientist — Quality & Pipeline Intelligence

Engai; Stati Uniti; Remoto; USD 130.000 - 165.000; Tempo pieno

Vantaggi offerti da questo lavoro

Remote role in the United States

Equity

Health, dental, and vision

Annual in-person global retreats

Foundation models and autonomous data

Professional growth

Descrizione del lavoro

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

Competenze

• 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.

Mansioni

• 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.

Conoscenze

Active learning

Anomaly detection

Statistical validation

Production collaboration

Formazione

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

Strumenti

Python

PyTorch

scikit-learn

Descrizione del lavoro

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.