Why This Role Stands Out
This on-site Data Scientist role at Dimension Consulting offers a unique opportunity to own the end-to-end data product lifecycle, from raw data to production AI agents, fostering significant technical and career growth. You'll thrive here if you enjoy a hybrid data science and engineering challenge, building robust, terabyte-scale pipelines and impactful predictive models for a cutting-edge AI-native marketing company. Apply now to join a dynamic team and contribute to innovative consumer intelligence solutions.
Quick Overview
Job Description
Job title – Data Scientist
Job Type – Full time, Onsite
Work location - New York, NY (Williamsburg, Brooklyn
Visa - Open to visa transfers (including OPT / H-1B transfers) and USC
Core Stack - Python, SQL, dbt, Dagster, Airflow, Databricks, Snowflake, Redshift, PostgreSQL, AWS, S3
About the client - Is an AI-native marketing and data company building agentic systems on top of a proprietary consumer graph covering more than 270 million U.S. consumers.
Job Description
The role sits close to the core data product and is intended for a high-intellectual horsepower generalist who can move between data engineering, applied modelling, and production delivery.
The Data Scientist will own the full path from messy raw data to trusted attributes and predictive models used by autonomous agents and enterprise customers.
This is not a notebook-only research role: the hiring team expects hands-on feature engineering, model estimation, deployment, and reliable production pipelines at terabyte scale.
What the Data Scientist Will Do
• Stitch disparate third-party datasets into unified 360-degree consumer profiles.
• Build and deploy probabilistic estimation models for attributes such as income, wealth, affinity, propensity, LTV, and lookalike audiences.
• Design and maintain feature-engineering pipelines that operate reliably at terabyte scale.
• Own custom enterprise data science work end-to-end, from raw data through deployed production models.
• Ensure model outputs are robust enough to be consumed autonomously by AI agents and enterprise workflows.
• Operate as a data science / data engineering hybrid rather than a narrow research or infrastructure specialist.
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