Data Scientist (Hybrid as needed)

About the Role

You will plan, implement, deploy, maintain/evaluate data‑driven solutions that will directly impact patients and healthcare delivery; communicate with adjacent engineering teams as well as non‑technical/clinical stakeholders to understand how their needs can be translated into data science solutions. You will validate models used in the healthcare setting to ensure that they are improving patient care and safety. Ensures that these projects and their outputs enhance clinical quality, patient safety, and institutional efficiency, focusing on all aspects of data science, including data gathering and wrangling, exploratory data analysis, data modeling and machine learning, and model implementation and evaluation. Provides expertise on mathematical concepts for the broader applied analytics team.

Responsibilities

• Involvement throughout the end‑to‑end lifecycle of complex data science projects/programs including problem scoping, due diligence, evaluation/validation/model design and development, deployment to production systems, monitoring prediction output, and measuring value
• Act as the bridge between technical and non‑technical team members and stakeholders with strong communication skills
• Support the data science team in technical and strategic analyses for decision making related to machine learning/AI
• Demonstrate enthusiasm for self‑directed learning
• Work collaboratively to design, implement and maintain data science models and applications
• Apply data science methodologies (predictive modeling, machine learning, data analytics, visualization, usability and design) to departmental, service line, and enterprise applications and functions
• Synthesize complex data‑related problems into actionable business and/or clinical strategy and communicate findings to appropriate end‑users and stakeholders
• Assist with the development of specifications to support the design of new or modified data science projects, focusing on data‑driven optimization, enhancement, and development
• Evaluate projects, systems, and initiatives at the department, service line, and enterprise level; ensure outputs enhance clinical quality, patient safety, and institutional efficiency across all data science activities
• Maintain knowledge of present and planned data science projects and serve as the voice of the customer in all project initiatives
• Serve as the link between clinical staff (customer) requirements and IS capabilities
• Ensure systems are implemented to support organizational initiatives and goals to improve patient care quality, maximize safety, and provide operational efficiencies
• Serve as a resource to leadership; demonstrate familiarity with current hospital information systems
• Operate under general guidance; work assignments are varied and require interpretation and independent decisions
• Perform related duties as required (essential functions under the ADA)

Qualifications

• Bachelor’s Degree in Computer Science, Informatics, Statistics, Engineering, Data Science, or related field (required); Master’s Degree (preferred)
• Minimum of two (2) years of post‑graduate training or experience involving quantitative data analysis (required); experience working with clinical data, data science, and machine learning (preferred)
• Working familiarity with basic medical and health information technology concepts, including standardized terminologies, ontologies, electronic health records, data warehousing, and business intelligence tools (required)
• Expertise in working with SQL relational databases and statistical or general programming languages (e.g., Python, R) (required)
• Deep understanding of statistical and predictive modeling concepts, machine‑learning approaches, clustering and classification techniques, and recommendation and optimization algorithms

Highly Preferred

• Clear and effective communication and presentation skills
• Comfortable presenting to non‑technical/clinical stakeholders and executive leadership
• Experience in data science projects with organizational‑level impact (healthcare related preferred)
• Experience with cloud computing (GCP, Vertex AI preferred)
• Experience with orchestration/ETL pipelines (e.g., Airflow, Prefect, Luigi, Kubeflow); preference for Airflow
• Experience with DevOps/MLOps (e.g., containerization, CI/CD, feature stores, data lineage)
• Strong/advanced proficiency in statistical analysis, machine learning techniques, and model evaluation
• Strong/advanced proficiency in modern machine learning frameworks (e.g., scikit‑learn) and deep learning frameworks (e.g., PyTorch)
• Strong/advanced experience in training/deploying embedding models, fine‑tuning large language models, retrieval‑augmented generation, prompt and context engineering, and evaluation of generative AI systems