Data Scientist / Senior Data Scientist

Veeva Systems is a mission-driven organization and pioneer in industry cloud, helping life sciences companies bring therapies to patients faster. As one of the fastest-growing SaaS companies in history, we surpassed $3B in revenue in our last fiscal year with extensive growth potential ahead.

At the heart of Veeva are our values: Do the Right Thing, Customer Success, Employee Success, and Speed. We're not just any public company – we made history in 2021 by becoming a public benefit corporation (PBC), legally bound to balancing the interests of customers, employees, society, and investors.

Join us in transforming the life sciences industry, committed to making a positive impact on its customers, employees, and communities.

Crossix Analytics is a business within Veeva dedicated to advancing healthcare marketing with analytics, innovative planning, targeting, measurement, and optimization solutions. Positioned at the center of big data, innovative technology, and multichannel media, Veeva Crossix provides life sciences companies with insights to help make strategic business decisions and drive improved patient outcomes.

We are looking for a high performing methodologist to join our team in New York. The right candidate enjoys deconstructing complex problems and building analytics products, prioritizing simplicity and efficacy over complexity.

You will serve as a key driver in our business’ commercial success, owning methodological and data science research end-to-end. The position is offered at multiple levels of seniority.

10+ Years of hands-on analytics and data science experience

Expert in Python and SQL

Demonstrated track record of designing & implanting analytical methodologies for products

Proficient with source control workflows like GitHub

Some experience working with big data tools like Spark and AWS

Strong written & verbal communication skills

Rx & Medical Claims data expertise

HCP data expertise

Demonstrated track record of designing and implementing causal inference methodologies

PySpark application development experience