Data Scientist

The role independently frames complex problems, develops and validates data science approaches, builds solutions, and assesses models and methods developed by internal teams and external partners. Working with clinical and operational stakeholders, engineering teams, and governance partners, the Data Scientist supports technical intake, feasibility assessment, solution design, implementation, and ongoing scientific review.

Responsibilities/Job Description:

The Data Scientist brings deep expertise in classical AI/ML, statistics, and advanced analytics to Fairview Health Services’ AI & Automation program. The role independently frames complex problems, develops and validates data science approaches, builds solutions, and assesses models and methods developed by internal teams and external partners.

 

Working with clinical and operational stakeholders, engineering teams, and governance partners, the Data Scientist supports technical intake, feasibility assessment, solution design, implementation, and ongoing scientific review. The role determines whether an approach is technically sound and appropriate for its intended use, identifies limitations, recommends corrections, and communicates evidence-based technical feedback. Generative AI and LLM capabilities complement the role’s core statistical and machine-learning expertise.

 

This role will serve as a primary scientific and technical resource for internally developed and partner-delivered AI/ML solutions. The position will support model development, independent validation, methodology review, experimentation, and technical challenge while helping establish how Fairview evaluates model performance, limitations, bias, and ongoing scientific validity.

 

Responsibilities

 

Intake and solution design. Translate clinical and operational challenges into testable questions, data requirements, scientific approaches, and measures of success. Assess technical feasibility and recommend suitable analytical or AI/ML approaches for internal and partner initiatives.

 

Model development and review. Develop and validate statistical and machine-learning models, or independently review and approve models developed by others. MLOps owns production deployment and operation; partner with MLOps to transition validated models into supported use. Illustrative use cases include risk prediction, readmissions, population health, forecasting, and capacity planning.

 

Scientific methods validation. Design reproducible analyses, experiments, and validation plans. Assess data quality, feature selection, assumptions, validation design, performance, and limitations to determine whether results are reliable and support the proposed use.

 

Partner and internal assurance. Review model methods, code, data assumptions, validation evidence, and reported results delivered by internal teams and external partners. Reproduce or challenge findings as needed and recommend corrections or additional evidence.

 

Scientific review and approval. Independently review and approve model methods and validation evidence within assigned scientific-review authority and established governance. Document acceptance criteria, findings, limitations, and remediation; coordinate clinical, security, and production-release decisions with designated owners.

 

Applied data science. Develop and validate approaches to clinical and operational problems using linear and other regression models, random forests, and other suitable methods. Apply statistics to evaluate interventions, programs, and workflows; use optimization, natural-language processing, generative AI, and large language models where appropriate.

 

Data and implementation partnership. Define feature, cohort, outcome, and data requirements with the AI Data Modeler, data engineering, and domain owners. Build reproducible analytical code and prototypes and partner with MLOps and IT teams to implement validated solutions in supported workflows.

 

Ongoing scientific validity. Define model-performance measures and review criteria with scientific and domain stakeholders. Interpret monitoring findings and recommend or develop recalibration, retraining, or methodological changes; partner with MLOps on controlled implementation.

 

Technical leadership and communication. Independently lead the scientific work on assigned initiatives. Explain results and limitations to clinical, operational, technical, and partner audiences; provide constructive pushback, mentor colleagues, and maintain clear methodological documentation.

 

Privacy and governance. Follow organizational privacy, security, regulatory, and model-governance requirements. Provide documented scientific evidence supporting responsible use and informed clinical and operational decisions.

 

Required Qualifications

• B.S./B.A. in a quantitative field such as Statistics, Computer Science, Engineering, Applied Mathematics, Data Science, Epidemiology, or Health Informatics; or
• A combination of education and experience equivalent to a bachelor’s degree in a quantitative field.
• Four years of data science, predictive-modeling, or advanced-analytics experience, including independent responsibility for complex analytical or scientific work.
• Demonstrated proficiency in analytical programming, advanced analytics and statistics, predictive modeling, machine learning, and data visualization.
• Strong proficiency in Python and/or R and experience working with large relational databases using SQL.
• Demonstrated experience developing and validating models or independently reviewing and validating models developed by others. Either experience route must demonstrate hands-on ability to implement analytical methods, test results, and critically assess scientific evidence.
• Deep knowledge of statistical reasoning, linear and other regression techniques, random forests, supervised and unsupervised learning, feature engineering, forecasting, model evaluation, and the selection and validation of data science methods.
• Ability to independently assess ambiguous problems, identify methodological limitations, recommend corrections, and communicate scientific and technical findings clearly.
• Ability to work effectively with clinical, operational, engineering, and external-partner teams; provide constructive technical feedback; manage multiple priorities; and support implementation of reproducible solutions.

Preferred Qualifications

• M.A./M.S. in a related quantitative field.
• Experience with healthcare/EHR data, including Epic Clarity or Caboodle, and clinical-quality or operational measures.
• Experience with outcomes research, clinical research, population health, causal inference, optimization, or evaluation of interventions and workflows.
• Experience developing or evaluating natural-language processing, generative AI, or large language model solutions.
• Experience with cloud-based development, containerized solutions, computational infrastructure, modern data platforms, and data visualization.
• Knowledge of healthcare regulatory and reimbursement environments

Qualifications:

$114,857.60- $162,177.60 Annual