Production Data Scientist: Feature Engineering & ML

Stealth Startup; New York (NY); On-site; USD 200,000 - 225,000; Full time

Benefits offered by this job

Relocation package

Competitive equity

Marquee benefits package

Job summary

Stealth Startup is seeking a Data Scientist who builds features and models powering a consumer graph and agents atop it. You own the path from raw data to production-ready features that agents can call at scale, shaping the foundation for other systems as we grow.

You’ll expand predictive coverage, improve income/wealth models, and contribute to a robust data lake while deploying pipelines and validating outputs for autonomous use.

Qualifications

• 2-4 years working as a data scientist, data engineer, or software engineer in a data-heavy context.
• Proficient in Python and SQL with a data-driven mindset.
• Reason from first principles and translate datasets into usable features.
• Strong data engineering intuition around cleaning, ingestion and modeling.
• Solid engineering background with production pipeline deployment experience.
• Willingness to work in the NYC office; relocation package provided.
• Flexible and eager to learn and grow with the team.

Responsibilities

• Create new features for models and agents and expand the predictive surface.
• Improve existing models with rigorous feature engineering on income/wealth signals.
• Contribute to building out the data lake and making terabytes of data queryable.
• Develop feature pipelines that operate reliably at terabyte scale, production-ready.
• Write validations and guardrails so agents can act autonomously.

Skills

Python

SQL

Data engineering

ML engineering

Tools

Dagster

dbt-core

Spark

Iceberg

Trino

AWS Glue

Modal

Job description

Stealth Startup is seeking a Data Scientist who builds features and models powering a consumer graph and agents atop it. You own the path from raw data to production-ready features that agents can call at scale, shaping the foundation for other systems as we grow.

You’ll expand predictive coverage, improve income/wealth models, and contribute to a robust data lake while deploying pipelines and validating outputs for autonomous use.