Job Summary and Qualifications

As part of the Accelerated Technologies team, the Data Scientist II is responsible for designing, developing, validating, and supporting analytical and machine learning solutions using clinical, operational, and business data.

This position works closely with cross-functional teams to translate business and clinical needs into well-defined analytical problems, scalable data products, statistical models, and actionable insights. The Data Scientist II will apply statistical analysis, machine learning, data engineering, and data visualization techniques to identify opportunities to improve patient outcomes, operational performance, data quality, and healthcare delivery.

The successful candidate will spend considerable time exploring complex healthcare datasets, evaluating data quality, developing predictive and descriptive models, designing experiments, and communicating analytical findings to both technical and non-technical stakeholders. A willingness to develop broad subject-matter expertise across clinical and operational domains is essential.

Success in this position requires a balance of technical expertise, analytical judgment, healthcare knowledge, communication skills, and the ability to build strong working relationships with business partners, clinical stakeholders, project teams, and other ITG teams.

GENERAL RESPONSIBILITIES

Perform exploratory, descriptive, predictive, and inferential analysis on complex clinical, operational, and business datasets.

Develop, validate, deploy, and support statistical and machine learning models that address clearly defined healthcare and business needs.

Translate clinical and operational questions into analytical hypotheses, modeling approaches, evaluation plans, and measurable outcomes.

Write efficient SQL queries and develop reusable datasets, features, and analytical pipelines using SQL, Python, R, or similar technologies.

Apply appropriate statistical methods to compare populations, treatments, interventions, and operational processes; identify trends; quantify uncertainty; and determine statistical significance.

Design and evaluate experiments, quasi-experiments, pilots, and observational studies when appropriate.

Select appropriate model evaluation metrics based on the intended clinical or business use case.

Perform model validation, error analysis, sensitivity analysis, bias assessment, and performance monitoring.

Develop interpretable analytical solutions and clearly explain model assumptions, limitations, risks, and intended use.

Partner with data engineering, product, clinical, business, and technology teams to operationalize analytical solutions.

Contribute to the development and support of production-grade data science products, services, and decision-support capabilities.

Develop high-level and detailed technical design specifications that support model development, deployment, monitoring, and troubleshooting.

Create clear documentation covering data definitions, analytical methods, model logic, assumptions, testing, limitations, and support procedures.

Identify and document data-quality issues and develop implementation strategies to improve data accuracy, completeness, consistency, and standardization.

Evaluate the usefulness, reliability, lineage, and cleanliness of data from multiple source systems.

Ensure clinical and analytical data is interpreted and presented within the appropriate clinical, operational, and business context.

Participate in requirements validation, feasibility analysis, solution design, and prioritization for data science products.

Estimate the level of effort required to deliver analytical models, experiments, data products, and supporting documentation.

Build dashboards, reports, visualizations, and analytical tools that help stakeholders understand model outputs and business performance.

Communicate analytical findings through written summaries, presentations, visualizations, and recommendations tailored to the intended audience.

Accurately communicate project status, analytical risks, model limitations, production issues, and dependencies to leadership.

Follow and promote software-development and model-development best practices, including version control, code review, reproducibility, documentation, and automated testing.

Develop and execute unit, integration, regression, performance, data-validation, and model-validation testing.

Support solutions throughout the development lifecycle, from discovery and design through deployment, monitoring, maintenance, and enhancement.

Research and become a subject-matter expert on assigned data domains, products, applications, models, source systems, and business workflows.

Monitor deployed models and analytical products for performance degradation, data drift, concept drift, unexpected outcomes, and operational issues.

Lead or participate in troubleshooting and root-cause analysis for data, model, pipeline, and production-support issues.

Resolve moderately complex to complex production issues associated with analytical applications and data products.

Identify opportunities to improve the scalability, maintainability, accuracy, reproducibility, and business value of analytical solutions.

Mentor junior data scientists, analysts, and other team members in analytical methods, coding practices, model evaluation, and healthcare-data concepts.

Provide technical guidance and peer review for analytical designs, code, statistical approaches, and model-development work.

Contribute to the development of team standards, reusable analytical frameworks, shared libraries, and data science best practices.

Work effectively both independently and as part of a multidisciplinary team.

Manage multiple priorities, establish realistic delivery dates, and consistently meet project commitments.

Build strong relationships within the department and with clinical, business, product, engineering, and project-team partners.

Take ownership of assigned responsibilities and work collaboratively to achieve team and organizational goals.

What qualifications you will need:

EDUCATION

Bachelor's degree in data science, statistics, mathematics, computer science, engineering, economics, epidemiology, biomedical informatics, public health, or a related quantitative field - Required

EXPERIENCE

Three or more years of professional experience in data science, advanced analytics, statistical modeling, machine learning, or a related field - Required

Healthcare, clinical, claims, electronic health record, or healthcare-operations data experience - Preferred

Strong SQL experience and demonstrated ability to work with large, complex datasets - Preferred

Professional experience using Python, R, or a comparable analytical programming language - Required

Experience developing and validating statistical or machine learning models- Required

Experience with data visualization or business intelligence tools such as Power BI, Qlik Sense, Tableau, or similar platforms - Preferred

Experience with relational and cloud-based data platforms such as BigQuery, SQL Server, PostgreSQL, Snowflake, or similar technologies

Experience using common data science libraries and frameworks for data preparation, statistical analysis, machine learning, and visualization

Experience with version-control tools and collaborative software-development practices

Experience deploying or supporting analytical models in a production environment - Preferred

Experience with cloud-based analytics, machine learning platforms, containerization, APIs, or model-serving technologies - Preferred

Experience working with structured healthcare data standards or clinical terminologies is Preferred

KNOWLEDGE, SKILLS, AND ABILITIES

Strong understanding of statistical analysis, hypothesis testing, experimental design, regression, classification, clustering, forecasting, and model evaluation.

Ability to determine when a machine learning solution is appropriate and when a simpler analytical approach is sufficient.

Ability to clean, transform, integrate, analyze, and visualize data from multiple source systems.

Ability to identify confounding factors, selection bias, data leakage, missing-data concerns, and other analytical risks.

Ability to develop scalable and maintainable analytical solutions that meet functional and non-functional requirements.

Strong understanding of data-quality, data-lineage, data-governance, and reproducible-research principles.

Ability to communicate technical findings, model behavior, uncertainty, and limitations to clinical, business, and technical audiences.

Ability to facilitate diverse groups of stakeholders in requirements gathering, analytical problem solving, and decision-making.

Strong analytical reasoning, problem-solving, and issue-resolution skills.

Strong written, verbal, presentation, and data-storytelling skills.

Strong interpersonal skills and demonstrated ability to work with diverse stakeholders on complex projects.

Ability to prioritize multiple activities and adapt to a rapidly changing environment.

Ability to produce accurate, well-documented, and high-quality analytical deliverables.

Ability to work independently with limited supervision while seeking input when appropriate.

Ability to mentor team members on statistical, analytical, programming, and machine learning concepts.

Ability to identify dependencies, analytical risks, and implementation challenges across multiple data products.

Strong attention to detail, intellectual curiosity, sound judgment, and commitment to continuous learning.

Understanding of responsible AI, model transparency, data privacy, and the appropriate use of sensitive healthcare information.

Benefits

HCA Healthcare, offers a total rewards package that supports the health, life, career and retirement of our colleagues. The available plans and programs include:

• Comprehensive benefits for medical, prescription drug, dental, vision, behavioral health and telemedicine services
• Wellbeing support, including free counseling and referral services
• Time away from work programs for paid time off, paid family leave, long- and short-term disability coverage and leaves of absence
• Savings and retirement resources, including a 401(k) Plan with a 100% match on 3% to 9% of pay (based on years of service), Employee Stock Purchase Plan, flexible spending accounts, preferred banking partnerships, retirement readiness tools, rollover support and financial wellbeing counseling
• Education support through tuition assistance, student loan assistance, certification support, dependent scholarships and a partnership with Galen College of Nursing
• Additional benefits for fertility and family building, adoption assistance, life insurance, supplemental health protection plans, auto and home insurance, legal counseling, identity theft protection and consumer discounts

Learn more about Employee Benefits

Note: Eligibility for benefits may vary by location.

If you find this opportunity compelling, we encourage you to apply for our Data Scientist opening. We promptly review all applications. Highly qualified candidates will be directly contacted by a member of our team. We are interviewing - apply today!