Interested in this Data Scientist role at Vanderbilt Health?
Apply Now →About This Role
Discover Vanderbilt University Medical Center : Located in Nashville, Tennessee, and operating at a global crossroads of teaching, discovery, and patient care, VUMC is a community of individuals who come to work each day with the simple aim of changing the world. It is a place where your expertise will be valued, your knowledge expanded, and your abilities challenged. Vanderbilt Health is committed to an environment where everyone has the chance to thrive and where your uniqueness is sought and celebrated. It is a place where employees know they are part of something that is bigger than themselves, take exceptional pride in their work and never settle for what was good enough yesterday. Vanderbilt’s mission is to advance health and wellness through preeminent programs in patient care, education, and research.
Organization:
MED Diabetes/Endocrinology
Job Summary:
Data Scientists perform detailed analyses of large bodies of heterogeneous data to discover new patterns and insights that impact patient health or augment human capabilities. Data Scientists have deep expertise in the methods used to analyze data and deep knowledge of data types, topics, and scientific challenges and approaches that will be used to help inform and define new products, experiences, and technologies. This position works in conjunction with Data Science and Informatics faculty to provide technical thought leadership and works closely with Informatics and IT teams to create data and intelligence\-driven systems to solve complex client problems.
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DEPARTMENT SUMMARY
The Division of Diabetes, Endocrinology and Metabolism has a rich tradition of clinical, investigative and training excellence. With over 70 faculty and staff members, the division participates in a wide range of clinical programs and clinical and basic science research. Training the next generation of physicians and scientists is one of our main missions and involves programs in Endocrinology and Obesity Medicine, as well as funded T32 and T35 training programs.
KEY RESPONSIBILITIES
- Participates in multidisciplinary teams to design, develop, and recommend new approaches for data\-driven decision\-making.
- Leads discovery processes on pioneering Artificial Intelligence (“AI")/Machine Learning (“ML”) based approaches to solve complex data problems across a variety of domains.
- Provides strategic direction regarding data science and AI in a specific area (e.g., cancer, surgery, planning services, marketing, healthcare delivery).
- Guides and educates data science teams to provide unbiased and accurate information, analysis, consultation, and recommendations to support strategic and operational data science initiatives.
- Develops analytics tools that can be used by other staff with non\-technical expertise.
- In some positions, may work with enterprise executive leadership to advance digital strategy.
- Makes strategic recommendations on data collection, integration and retention requirements, incorporating business requirements and knowledge of best practices.
- Supports scientific projects under the supervision of a designated senior level data scientist or self\-direction.
- Designs, develops, applies, and modifies scripts or software applications to support data management, extraction, and analysis.
- Provides consultative services and is responsible to gather, analyze, present findings to leadership.
TECHNICAL CAPABILITIES
Our Academic Enterprise is one of the nation’s longest serving and most prestigious academic medical centers. Through its historic bond with Vanderbilt University and integral role in the School of Medicine, Vanderbilt Health cultivates distinguished research and educational programs to advance a clinical enterprise that provides compassionate and personalized care and support for millions of patients and family members each year.
World\-leading academic departments and comprehensive centers of excellence pursue scientific discoveries and transformational educational and clinical advances across the entire spectrum of health and disease.
Aligning with Vanderbilt Health’s Strategic Directions , the Office of Research provides shared research resources, administrative expertise and professional guidance to enable the trans\-disciplinary environment and highly collaborative culture that advances discovery and training for all the research faculty, trainees, students and staff.
Core Accountabilities:
Organizational Impact: Delivers job responsibilities that impact own job area/team with some guidance. Problem Solving/ Complexity of work: Uses existing procedures, research and analysis to solve standard job related problems that may require some judgement. Breadth of Knowledge: Requires subject matter knowledge within a professional area to meet job requirements. Team Interaction: Individually contributes to project/ work teams.
Core Capabilities :
Supporting Colleagues: \- Develops Self and Others: Continuously improves own skills by identifying development opportunities.\- Builds and Maintains Relationships: Seeks to understand colleagues priorities, working styles and develops relationships across areas. \- Communicates Effectively: Openly shares information with others and communicates in a clear and courteous manner. Delivering Excellent Services: \- Serves Others with Compassion: Invests time to understand the problems, needs of others and how to provide excellent service. \- Solves Complex Problems: Seeks to understand issues, solves routine problems, and raises proper concerns to supervisors in a timely manner. \- Offers Meaningful Advice and Support: Listens carefully to understand the issues and provides accurate information and support. Ensuring High Quality: \- Performs Excellent Work: Checks work quality before delivery and asks relevant questions to meet quality standards.\- Ensures Continuous Improvement: Shows eagerness to learn new knowledge, technologies, tools or systems and displays willingness to go above and beyond. \- Fulfills Safety and Regulatory Requirements: Demonstrates basic knowledge of conditions that affect safety and reports unsafe conditions to the appropriate person or department. Managing Resources Effectively :\- Demonstrates Accountability: Takes responsibility for completing assigned activities and thinks beyond standard approaches to provide high\-quality work/service. \- Stewards Organizational Resources: Displays understanding of how personal actions will impact departmental resources. \- Makes Data Driven Decisions: Uses accurate information and good decision making to consistently achieve results on time and without error. Fostering Innovation: \- Generates New Ideas: Willingly proposes/accepts ideas or initiatives that will impact day\-to\-day operations by offering suggestions to enhance them.\- Applies Technology: Absorbs new technology quickly; understands when to utilize the appropriate tools and procedures to ensure proper course of action. \- Adapts to Change: Embraces changes by keeping an open mind to changing plans and incorporates change instructions into own area of work.
Position Qualifications:
Responsibilities:
Certifications :
Work Experience :
Relevant Work Experience
Experience Level :
3 years
Education :
Master's
*This role offers the opportunity to make a meaningful impact within Vanderbilt Health, supported by a comprehensive benefits package which may include health, disability, retirement and/or wellness offerings to enhance your well\-being and professional growth.*
*Vanderbilt Health is committed to fostering an environment where everyone has the chance to thrive and is committed to the principles of equal opportunity. EOE/Vets/Disabled.*
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Vanderbilt 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 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Vanderbilt Health AI Hiring
Vanderbilt Health has 1 open AI role right now. They're hiring across Data Scientist. Based in Nashville, TN, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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