Interested in this Data Scientist role at Veeva Systems?
Apply Now →About This Role
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 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 — 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 — 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
We value your time and believe in a transparent hiring process. Here is the process you can expect.
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.
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 \- $140,000
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.
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Veeva’s headquarters is located in the San Francisco Bay Area with offices in more than 15 countries around the world.
Veeva is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity or expression, religion, national origin or ancestry, age, disability, marital status, pregnancy, protected veteran status, protected genetic information, political affiliation, or any other characteristics protected by local laws, regulations, or ordinances. If you need assistance or accommodation due to a disability or special need when applying for a role or in our recruitment process, please contact us at talent\_accommodations@veeva.com.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $95K-$140K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Veeva Systems, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills in Demand for This Role
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($117K) sits 39% below the category median. Disclosed range: $95K to $140K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Veeva Systems AI Hiring
Veeva Systems has 2 open AI roles right now. They're hiring across Data Scientist, AI Product Manager. Based in Remote, US. Compensation range: $140K - $165K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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