Senior Data Scientist, ML for Identity & Measurement

LiveRamp; New York (NY); On-site; USD 130,000 - 197,000; Full time

Job summary

LiveRamp is seeking a data scientist to design, implement, and iterate production-grade machine learning and statistical models that power core identity, entity resolution, and measurement capabilities across our data collaboration platform.

You will transform large, high‑dimensional datasets, own end‑to‑end data science workflows from framing to deployment, collaborate with engineering and product teams, and communicate complex results clearly to stakeholders while building maintainable code,

Qualifications

• MS or PhD in a quantitative field or equivalent practical experience.
• 3+ years designing and deploying data science or ML solutions in production.
• Proficiency in Python and SQL with common ML libraries.

Responsibilities

• Design, implement, and iterate production-grade ML models powering identity, entity resolution, and measurement.
• Analyze large, high-dimensional datasets to uncover insights and improve model performance.
• Own end-to-end data science workflows from problem framing to deployment and monitoring.
• Translate complex analyses into clear recommendations for product, engineering, and go-to-market teams.
• Define success metrics and build evaluation frameworks to quantify business impact.

Skills

Python

SQL

Data analysis

Experimentation

Communication

Education

MS/PhD in CS/ML/Statistics/Applied Math

Tools

BigQuery

Google Cloud Platform

Vertex AI

Dataflow

pandas

NumPy

scikit-learn

Job description

LiveRamp is seeking a data scientist to design, implement, and iterate production-grade machine learning and statistical models that power core identity, entity resolution, and measurement capabilities across our data collaboration platform.

You will transform large, high‑dimensional datasets, own end‑to‑end data science workflows from framing to deployment, collaborate with engineering and product teams, and communicate complex results clearly to stakeholders while building maintainable code,