Senior Data Scientist, Life Sciences Marketing Analytics

Publicis Groupe; New York (NY); Hybrid; USD 112,000 - 180,000; Full time

Job summary

Publicis Groupe in the United States is seeking a hands-on Data Scientist who will support the Vice President, Data Science by turning inputs into rigorous analyses, models, and deliverables for pharmaceutical, biotech, and medical technology clients.

This role thrives in a fast-moving environment, requiring clear data storytelling, collaboration with cross-functional teams, and ownership of projects from data to production-ready outputs that inform marketing activation and client

Qualifications

• 3+ years of data science or predictive analytics in life sciences or healthcare.
• Proficiency in Python and/or R; strong SQL for large datasets.
• Experience building ML models with production deployment.
• Strong communication and ability to translate findings for business stakeholders.
• Bachelor's degree in Data Science, Statistics, CS, Mathematics; Master's preferred.

Responsibilities

• Build and refine ML models and predictive analytics for life sciences marketing challenges.
• Contribute across the ML pipeline: data acquisition, feature engineering, model training, and validation.
• Work within a complex, multi-source data environment (claims, prescribing, real-world, and consumer data).
• Monitor data quality and maintain data science pipelines for client data assets.
• Summarize findings and model results for senior team members and client-facing materials.

Skills

Python

R

SQL

Communication

Data storytelling

Education

Bachelor's degree in Data Science/Statistics/CS or related

Master's degree preferred

Tools

AWS

Databricks

Snowflake

Job description

Publicis Groupe in the United States is seeking a hands-on Data Scientist who will support the Vice President, Data Science by turning inputs into rigorous analyses, models, and deliverables for pharmaceutical, biotech, and medical technology clients.

This role thrives in a fast-moving environment, requiring clear data storytelling, collaboration with cross-functional teams, and ownership of projects from data to production-ready outputs that inform marketing activation and client