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About This Role
Introduction
Do you want to join an organization that invests in you as a Lead Data Scientist? At HCA, you come first. HCA Healthcare has committed up to $300 million in programs to support our incredible team members over the course of three years.
Benefits
Work from Home, offers a total rewards package that supports the health, life, career and retirement of our colleagues. The available plans and programs include:
- Comprehensive medical coverage that covers many common services at no cost or for a low copay. Plans include prescription drug and behavioral health coverage as well as free telemedicine services and free AirMed medical transportation.
- Additional options for dental and vision benefits, life and disability coverage, flexible spending accounts, supplemental health protection plans (accident, critical illness, hospital indemnity), auto and home insurance, identity theft protection, legal counseling, long-term care coverage, moving assistance, pet insurance and more.
- Free counseling services and resources for emotional, physical and financial wellbeing
- 401(k) Plan with a 100% match on 3% to 9% of pay (based on years of service)
- Employee Stock Purchase Plan with 10% off HCA Healthcare stock
- Family support through fertility and family building benefits with Progyny and adoption assistance.
- Referral services for child, elder and pet care, home and auto repair, event planning and more
- Consumer discounts through Abenity and Consumer Discounts
- Retirement readiness, rollover assistance services and preferred banking partnerships
- Education assistance (tuition, student loan, certification support, dependent scholarships)
- Colleague recognition program
- Time Away From Work Program (paid time off, paid family leave, long- and short-term disability coverage and leaves of absence)
- Employee Health Assistance Fund that offers free employee-only coverage to full-time and part-time colleagues based on income.
Learn more about Employee Benefits
Note: Eligibility for benefits may vary by location.
You contribute to our success. Every role has an impact on our patients’ lives and you have the opportunity to make a difference. We are looking for a dedicated Lead Data Scientist like you to be a part of our team.
Job Summary and Qualifications
Job Summary
At HCA Healthcare, we are committed to providing our patients and care providers with the highest quality of healthcare services. HCA’s Care Transformation and Innovation (CT&I) team delivers a step change in that direction through clinically led integration of digital and AI technologies into care.
Data Scientists within CT&I will play a critical role in helping us achieve this goal by actively solving for and implementing a broad range of data science solutions to drive transformational change. This includes delivery of data science products from incubation to deployment to monitoring; including solutioning for the business need, data analysis, feature engineering, building and validating models, deploying solutions to enterprise production platforms, and monitoring model performance and reliability.
This individual will be responsible for actively delivering production grade data science products; including implementing best practices, frameworks, tooling, and documentation.
They will be expected to bring hands-on expertise in predictive analytics, classification, image recognition, NLP, anomaly detection, machine learning, EDA, feature engineering, optimization, statistics, and generalized business problem solving by creatively applying AI/ML.
What you will do:
* DATA INJECTION: Design and implement incoming data for feature engineering and machine learning
- FEATURE CREATION AND TRANSFORMATION: Create features with usable predictive power by using domain knowledge of healthcare data, statistics, and data science
- FEATURE EXTRACTION AND SELECTION: Extract features via cluster analysis, text analytics, principal components analysis and related methodologies to identify useful information without distorting original relationships or significant information in order to performance tune and promote scalability of the model
- EXPLORATORY DATA ANALYSIS: Conduct data exploration and analysis to identify relevant patterns, trends, and relationships; understand the data deeply to appropriately align the data features with the appropriate machine learning methodologies
- MODEL EXPERIMENTATION: Apply various statistical and machine learning techniques to develop, tune, and optimize predictive modeling to align with the use case requirements
- DELIVERY: Deploy code using best practices
- DOCUMENTATION: Document and present data science strategies and insights and solutions
- MONITORING: Build and maintain pipelines, code, and processes to monitor the proper functioning of enterprise grade data science products
- DATA AND OUTPUT QUALITY: Ensure high quality and integrity throughout the entire data science process
- PRODUCT DEVELOPMENT MINDSET: Collaborate with stakeholders, data scientists, data engineers, and product managers to build and deliver high-quality data products
What qualifications you will need:
- 6+ years of overall experience in various aspects of data science and machine learning (for Lead level)
- Expert experience in SQL and Python
- Experience with structured and unstructured data (ie. tabular, text, images, video, etc)
- Experience with SQL relational and non-relational databases
- Experience with data processing and ETL tools (e.g Apache Spark)
- Experience with data visualization and data monitoring tools
- Experience with modern software engineering practices (e.g. automated testing, continuous integration and continuous development)
- Experience with Docker
- Experience with cloud deployments in GCP or another cloud platform
- Experience with Agile development methods and tools (e.g. Azure Dev Ops)
- Excellent communication and collaboration skills, with the ability to work effectively across multiple teams and stakeholders
- Ability to tell a story using data and insights that drives action and change
- Domain knowledge of healthcare data preferred
- Expertise in healthcare protocols and formats such as HL7, FHIR, DICOM preferred
- Expert in the regulatory aspects of the healthcare domain preferred
- Experience in GCP platform preferred
"Good people beget good people."- Dr. Thomas Frist, Sr.
HCA Healthcare Co-Founder
We are a family 270,000 dedicated professionals! Our Talent Acquisition team is reviewing applications for our Lead Data Scientist opening. Qualified candidates will be contacted for interviews. Submit your resume today to join our community of caring!
We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Work From Home, 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 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Work From Home AI Hiring
Work From Home has 1 open AI role right now. They're hiring across Data Scientist. Based in Nashville, TN, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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