Senior 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. As a Work Anywhere company, we support your flexibility to work from home or in the office, so you can thrive in your ideal environment. Join us in transforming the life sciences industry, committed to making a positive impact on its customers, employees, and communities.

The Role

As a Senior Data Scientist on the Veeva Compass Patient team, you will be a founding data scientist that owns the greenfield design methodologies and models that tackle large scale complex health data issues. An example of opportunities for this role include:

• Tackle the unwinding of the "Super Patient" anomaly, a data-merging challenge causing abandonment of high-value clinical transactions. This person will be excited about having complete latitude to pioneer creative, inference-based models—leveraging techniques ranging from token entropy scoring to clinical transaction pattern recognition—to successfully unravel complex identity overlaps and salvage mission-critical data without relying on the raw PHI.
• Build a support structure for natural language search interface on top of medical and pharmacy claims data, for users to query data outside of traditional SQL-like query tools. This person will be excited about applying NLP, ML, and AI principles on a large data set and returning accurate, performant results across a variety of health data attributes.

You are excited about statistics and data science at scale on big data and taking billions of records to build algorithms in a computationally efficient and statistically effective manner while being able to keep the business problems we are working to solve in mind. You’ll work closely with Product Managers, Data Analysts, and Engineering teams to design and deliver features, including performance measurement, anomaly detection, and quality monitoring. This is a great opportunity to be at the forefront of innovation and help us solve some of the most challenging problems in healthcare data and analytics.

What you'll do

• Design and implement statistical models and data science solutions to tackle large-scale health data challenges.
• Collaborate with cross-functional teams (Product, Data Analytics, Engineering) to define problem statements, success metrics, and deliverables.
• Develop and validate inference-based models, NLP/AI solutions, and scalable data processing pipelines.
• Experiment with novel techniques to address identity resolution, data merging, and data quality issues while preserving privacy.
• Monitor model performance, conduct quality assurance, and iterate based on feedback and measurement results.

Who you are

• Strong background in statistics, machine learning, and data science with experience applying these skills to large healthcare-related datasets.
• Proficient in Python (or similar) and modern data science toolkits; experience with NLP, ML, AI on big data is a plus.
• Ability to work with stakeholders across Product, Analytics, and Engineering to translate business problems into modeling solutions.
• Comfort with greenfield projects, experimentation, and working with minimal supervision.

Tech stack

• Python, ML libraries, NLP tools
• Big data processing and analytics capabilities
• Collaboration with Product Managers, Data Analysts, and Engineering teams

Team description

Partnering with Product Managers, Data Analysts, and Engineering teams to design and deliver features, including performance measurement, anomaly detection, and quality monitoring.