Interested in this Data Scientist role at Versant Health?
Apply Now →Skills & Technologies
About This Role
Senior Data Scientist
Who are we?
Versant Health is one of the nation’s leading administrators of managed vision care, serving millions of our clients’ members nationwide. We are driven by our mission to help members enjoy the wonders of sight through healthy eyes and vision.
As a Versant Health associate, you can enjoy a comprehensive Total Rewards package, which includes health and dental insurance, tuition reimbursement, 401(k) with company match, pet insurance, no-cost-to-you vision insurance for you and your qualified dependents. We are also invested in your success. There are many opportunities for advancement and development throughout all stages of your career with us.
See how you can make a difference with the support of strong leadership and a team environment.
See Everything, Be Anything™.
What are we looking for?
The Senior Data Scientist is a principal level individual contributor responsible for architecting, delivering, and governing enterprise grade advanced analytics and machine learning solutions that materially influence strategic decisions, financial performance, operational efficiency, and regulatory posture.
This role operates at the intersection of data science, data engineering, cloud analytics platforms, and business strategy, serving as a technical authority and thought leader across complex, high risk analytical initiatives—particularly within regulated healthcare environments.
The Senior Data Scientist is accountable not only for model development, but for explainability, stability, auditability, and long term operational viability of analytical solutions deployed at scale within the Microsoft data ecosystem.
Where you will have an impact
- Architect, design, and deploy advanced statistical, predictive, and machine learning solutions that support enterprise level decision making across healthcare, operations, finance, and customer/member domains.
- Own end to end analytical solution lifecycle, including data discovery, feature engineering, modeling, validation, performance monitoring, retraining strategy, and retirement planning.
- Serve as a technical authority on model explainability, fairness, robustness, and regulatory defensibility—particularly where ground truth or forward looking forecasts are limited.
- Design analytics solutions natively within the Microsoft cloud analytics stack, ensuring alignment to enterprise data architecture and governance standards.
- Partner closely with executive stakeholders, data engineering leaders, and platform owners to translate complex business questions into high impact, production ready analytics.
- Lead and influence analytics standards without direct authority.
- Define best practices for: model documentation, audit artifacts, model risk management, stability analysis, reproducibility, and controlled experimentation.
- Guide and review work produced by other data scientists, analysts, and vendors to ensure technical rigor and business relevance.
- Present complex analytical findings to senior leadership using clear narratives, decision oriented visuals, and quantified trade offs.
- Proactively identify analytical risks, data limitations, and downstream impacts of modeling decisions.
- Continuously evaluate emerging tools, methodologies, and Microsoft platform capabilities to drive modernization and performance gains.
What’s necessary to do the job?
- Master's degree in data science, statistics, mathematics, computer science, engineering, or related field required.
- 7–10+ years of progressive experience delivering advanced analytics or machine learning solutions in complex, data rich environments.
- Required: Prior experience in healthcare analytics (payer, provider, life sciences, or regulated healthcare data domains).
- Prior acutrary experience a plus.
- Proven experience deploying production analytics or ML solutions supporting mission critical business processes.
- Python (pandas, NumPy, scikit learn, PyTorch/TensorFlow where appropriate)
- SQL (advanced T SQL optimization, analytical patterns)
- ML explainability and governance techniques (e.g., SHAP style approaches, stability testing)
- Deep, hands-on, production experience across sizable portion of the Microsoft analytics ecosystem, including:
- Microsoft Fabric (Lakehouse, Data Warehouse, Real-Time Analytics, Notebooks)
- Azure Synapse Analytics (dedicated & serverless SQL pools)
- Azure Data Factory (Fabric Data Pipelines)
- Azure Data Lake Storage
- Azure Machine Learning (model training, deployment, monitoring)
- Power BI (semantic models, DAX optimization, executive dashboards)
- Microsoft Purview (data cataloging, lineage, classification)
HIPAA & Security Requirements
All Associates must comply with the Health Insurance Portability Accountability Act of 1996 (HIPAA) as it pertains to disclosures of protected health information (PHI) as described in the Notice of Privacy Practices and HIPAA Privacy Policies and Procedures. As a component of job roles and responsibilities, Associates may have access to covered information, cardholder data or other confidential customer information which must be protected at all times. As a result, Associates must explicitly adhere to all data security guidelines established within the Company’s Privacy & Security Training Program.
We offer a comprehensive and competitive total rewards package designed to support your health, financial well being, and work life balance. Benefits include medical, dental, and paid vision coverage; paid time off and company holidays; retirement savings with employer contribution; employee wellness resources; and professional development opportunities. Additional benefits may include flexible work arrangements, employee assistance programs, and other programs that support you both at work and beyond.
This role is compensated through a fixed annual salary. The expected salary range for this position is $170,000.00 – $180,000.00 annually, based on role scope, experience, and market considerations. This position is not eligible for bonus or incentive compensation.
Versant Health will never request money from candidates who seek employment with us and will never ask for any payment as part of the recruitment process.
Versant Health is a proud Equal Employment Opportunity and Affirmative Action employer dedicated to attracting, retaining, and developing a diverse and inclusive workforce. All qualified applicants will receive consideration for employment at Versant Health without regards to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, national origin, marital or domestic/civil partnership status, genetic information, citizenship status, uniformed service member or veteran status, or any other characteristic protected by law.
Salary Context
This $170K-$180K range is above the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Versant Health, 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 Required
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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($175K) sits 8% below the category median. Disclosed range: $170K to $180K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Versant Health AI Hiring
Versant Health has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $180K - $180K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.