Senior Data Scientist

Veeva Systems•New York - New York City, NY

5d•Remote

About The Position

Veeva Systems is a mission-driven public benefit corporation that develops industry cloud solutions for life sciences companies. As a Data Scientist on the Link Key Accounts team, you will develop models and methodologies that turn multi-source healthcare data into reliable intelligence about healthcare organizations, their networks, and decision-makers. The work spans quantitative analysis of claims and financial data as well as qualitative inference, including building organizational charts and mapping influence from titles, roles, and other signals. This is an early, high-latitude role with the opportunity to shape both the methods and the product. The role involves developing statistical and machine learning approaches for healthcare data, evaluating and combining heterogeneous data sources, and creating signals that support account targeting and segmentation. You will collaborate with Product, Data Engineering, Engineering, and QA to move solutions from prototype to production and continue improving them. Veeva is a Work Anywhere company, supporting flexibility to work from home or in the office. The listed base salary range is $95,000–$175,000; actual compensation may vary based on experience and location. The position may also be eligible for variable bonus and/or stock bonus.

Requirements

At least 5 years of hands-on data science and statistics experience.
At least 5 years working with healthcare data and the US healthcare system.
Experience with healthcare claims data (medical and/or pharmacy) and metrics derived from it.
Experience with healthcare organizational data, including HCO/HCP, IDN, GPO, and payer data.
Experience developing clinical ontologies or standardized healthcare data models.
Experience with entity resolution, record linkage, or fuzzy matching on real-world, imperfect data.
Track record of inferring structure from noisy, incomplete data, including cleansing, experiment design, solution assessment, and scaling.
Comfort with ambiguity and ability to turn goals into tangible work plans.
Ability to explain statistical and technical concepts to audiences with varying levels of technical depth.

Nice To Haves

M.S. in Statistics, Computer Science, Machine Learning, Mathematics, or another quantitative discipline.

Responsibilities

Apply statistical and machine learning techniques to healthcare claims and other structured data to develop metrics and methodologies, including readmissions, average length of stay, all-payer estimates, and clinical ontologies.
Transform public financial data into clear signals and a coherent account of healthcare organizations’ financial health.
Develop entity-resolution and record-linkage models across health systems, provider organizations, employers, and payers.
Map affiliations and networks, inferring how organizations and people connect and how influence and reporting structures flow.
Infer organizational structure from qualitative signals, creating organizational charts and decision-maker maps from titles, positions, and role characteristics.
Design data-sourcing methodologies to evaluate, score, and combine public filings, web sources, and licensed data into accurate, defensible account signals.
Build models for account targeting and segmentation, including deciling, patient-volume and disease-state signals, and strategic filters.
Partner with Product, Data Engineering, Engineering, and QA to take solutions from prototype to production, then iterate and enhance them.