Data Scientist

๐Ÿข Lifelancer ยท all Lifelancer jobs (1758)
๐Ÿ“ United States
๐Ÿ’ฐ USD 95,000 - 140,000 / annual
๐Ÿ“… Posted 2026-09-10 ยท via Himalayas
๐Ÿท Data-Scientist,Healthcare-Data-Scientist,Healthcare-Analytics,Life-Sciences-Analytics,Data-Science,Search-Data-Scientist,AI-Data-Scientist,Data-Science-Specialist,ML-Data-Scientist,Statistical-Data-Scientist,Research-Data-Scientist,Data-Scientist-Analytics
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Job Title: Data Scientist

Job Location: Remote

Job Location Type: Remote

Job Contract Type: Full-time
Job Seniority Level:
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 โ€“ 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 Data Scientist on the Link Key Accounts team, you'll build the models and methodologies that turn multi-source healthcare data into reliable intelligence about healthcare organizations, their networks, and the people who make decisions inside them. The work spans both ends of the spectrum: quantitative analysis of claims and financial data, and qualitative inference โ€” building org charts and mapping influence from titles, roles, and other soft signals. This is an early, high-latitude role with room to shape both the methods and the product.
What You'll Do

- Apply statistical and machine learning techniques to healthcare claims and other structured data to build new metrics and methodologies โ€” for example, calculating readmissions and average length of stay across varied claims sources, producing all-payer estimates, and developing clinical ontologies

- Transform public financial data into clear signals and a coherent story about the financial health of healthcare organizations

- Build entity resolution and record-linkage models across health systems, provider organizations, employers, and payers

- Develop affiliation and network mapping โ€” inferring how organizations and people connect, and how influence and reporting structures flow across them

- Infer organizational structure from qualitative signals, building org charts and decision-maker maps from titles, positions, and role characteristics

- Design data-sourcing methodologies that evaluate, score, and combine heterogeneous inputs (public filings, web sources, licensed data) into accurate, defensible account signals

- Build the models that power account targeting and segmentation โ€” deciling, patient-volume and disease-state signals, strategic filters

- Partner with Product, Data Engineering, Engineering, and QA to take solutions from prototype to production, then iterate and enhance

Requirements

- 5+ years of hands-on data science and statistics experience

- 5+ years working with healthcare data and the US healthcare system

- Experience with healthcare claims data (medical and/or pharmacy) and the metrics derived from it

- Experience with healthcare organizational data (HCO/HCP, IDN, GPO, payer)

- 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 - cleansing, experiment design, solution assessment, and scaling

- Comfortable with ambiguity and turning goals into tangible work plans

- Able to explain statistical and technical concepts to audiences of varying technical depth

Nice to Have
- M.S. in Statistics, Computer Science, Machine Learning, Mathematics, or another quantitative discipline

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