What You'll Do
- Apply statistical and machine learning techniques to healthcare claims and other structured data to build new metrics and methodologies — 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 — 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 — 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
Interviewing with Veeva
- Follow the application process and submit your resume.
- Within 3 days, you will receive a link to a personality assessment administered by a third party.
- Once you complete the assessment, our team will review your full application package and follow up via email with our decision.
- If moving to the interview stage, the process is as follows:
- A conversation with the hiring manager
- A practical case exercise
- A final conversation with our group's Senior Leader.
- Once all interviews are complete, the manager will be in touch with a final decision.
We value your time and believe in a transparent hiring process. Here is the process you can expect.
Perks & Benefits
- Medical, dental, vision, and basic life insurance
- Flexible PTO and company paid holidays
- Retirement programs
- 1% charitable giving program
Compensation
- Base pay: $95,000 - $175,000
- The salary range listed here has been provided to comply with local regulations and represents a potential base salary range for this role. Please note that actual salaries may vary within the range above or below, depending on experience and location. We look at compensation for each individual and base our offer on your unique qualifications, experience, and expected contributions. This position may also be eligible for other types of compensation in addition to base salary, such as variable bonus and/or stock bonus.
#LI-RemoteUS#LI-Associate
Skills Required
- 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 including 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
- M.S. in Statistics, Computer Science, Machine Learning, Mathematics, or another quantitative discipline
Veeva Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Veeva and has not been reviewed or approved by Veeva.
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Equity Value & Accessibility — Equity is broadly distributed across the company, positioning most employees as shareholders. Stock-based incentives can materially enhance total compensation, particularly at higher seniority.
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Flexible Benefits — Work Anywhere enables remote-first flexibility with options to use offices and to gather through offsites or coworking weeks. This structure supports collaboration while minimizing rigid on-site requirements.
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Healthcare Strength — Core coverage includes medical, dental, vision, HSA/FSA, life and disability, and EAP. Supplemental perks such as commuter assistance and wellness or gym reimbursements are available in some locations.
Veeva Insights
What We Do
Veeva is the global leader in cloud software for the life sciences industry. Committed to innovation, product excellence, and customer success, Veeva serves more than 1,000 customers, ranging from the world’s largest pharmaceutical companies to emerging biotechs. As a Public Benefit Corporation, Veeva is committed to balancing the interests of all stakeholders, including customers, employees, shareholders, and the industries it serves.
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