Senior Data Scientist - Life Sciences & Marketing

Publicis Groupe Holdings B.V; New York (NY); On-site; USD 112,000 - 180,000; Full time

Job summary

Publicis Groupe seeks a Data Scientist to support the VP of Data Science with hands-on modeling, analysis, and deliverables for pharmaceutical clients. You will work across HCP targeting, patient and audience analytics, and marketing activation in a fast-paced, ambiguous environment.

Responsibilities include building ML models, data pipeline work, and collaboration with cross-functional teams to deliver actionable client insights while maintaining clear documentation and reproducibility.

Qualifications

• 3+ years of experience in data science, predictive analytics, or a related quantitative discipline.
• Proficient in Python and/or R and SQL for modeling and querying large datasets.
• Experience building ML models — supervised and unsupervised methods, with exposure to production deployment.
• Strong communication and data storytelling skills; translate analytical findings into actionable recommendations for stakeholders.

Responsibilities

• Build, test, and refine ML models for life sciences commercial and marketing challenges.
• Contribute across the ML pipeline — data acquisition, feature engineering, model training, and validation.
• Work within multi-source data environments (claims, prescribing, real-world, and consumer) and flag data gaps.
• Collaborate with brand, account, and activation teams to translate analyses into client recommendations.
• Document work and maintain reproducible code and tools.

Skills

Python

R

SQL

Data storytelling

Ambiguity handling

Education

Bachelor's degree in Data Science/Statistics/CS/Math

Master's preferred

Tools

AWS

Databricks

Snowflake

Job description

Publicis Groupe seeks a Data Scientist to support the VP of Data Science with hands-on modeling, analysis, and deliverables for pharmaceutical clients. You will work across HCP targeting, patient and audience analytics, and marketing activation in a fast-paced, ambiguous environment.

Responsibilities include building ML models, data pipeline work, and collaboration with cross-functional teams to deliver actionable client insights while maintaining clear documentation and reproducibility.