Top Healthtech Data Science Jobs

Leads advanced analytics, machine learning, statistical modeling, and NLP solution development using healthcare and operational data. Builds scalable pipelines, visualizations, and deployed models on Google Cloud Platform, translates findings for clinical and executive leaders, designs experiments, and establishes performance metrics. Requires collaboration across clinical, technical, and data engineering teams, with occasional on-site travel for executive presentations.

Leads Central Health System’s data science team and serves as its senior technical authority. Develops and validates predictive, machine learning, NLP, and forecasting models supporting population health, care management, and clinical decision-making. Oversees AI governance, model risk assessment, fairness, reproducibility, documentation, and regulatory alignment. Partners with clinical, data engineering, governance, and analytics teams while mentoring staff and managing advanced analytical standards across Epic, Azure, Snowflake, and other enterprise data sources.

Builds and maintains analysis-ready clinical and biological datasets from SQL, EDC, laboratory, registry, and biobank sources. Develops reproducible Python and SQL pipelines, applies machine learning to serologic and clinical data, and evaluates LLM-assisted workflows. Partners with biostatistics, data engineering, and clinical affairs on data quality, clinical data management, regulatory deliverables, study operations, dashboards, manuscripts, and clinical research documentation.

Lead computational analysis for research on intrinsically disordered proteins and regions. Develop reproducible pipelines, integrate HDX-MS, interactomics, cryo-EM, and molecular-dynamics datasets, apply statistical and machine-learning methods, create visualization tools, support experimental design, and collaborate with structural biology, proteomics, and computational modeling teams. Contribute to publications, technical reports, and open computational resources.

Leads healthcare data science initiatives from solution architecture through implementation. Partners with system leaders, stakeholders, analysts, and technical teams to develop predictive, forecasting, optimization, machine learning, and analytics solutions. Presents insights to technical and non-technical audiences, supports operational adoption, and leads delivery using agile methods.

Build and deploy scalable healthcare actuarial models within production software and AI systems. The role involves actuarial governance, model validation, data pipeline integration, AI evaluation, rapid prototyping, and technical collaboration with Product, Engineering, and Delivery teams. Candidates must translate complex healthcare risk and claims data problems into reproducible software solutions using Python, R, and SQL.

Develop and validate statistical and machine learning models that solve business challenges across R&D, manufacturing, finance, supply chain, and commercial functions. Translate complex data into actionable insights, optimize processes, support drug discovery and clinical analysis, improve forecasting and quality control, and communicate findings through data storytelling. Collaborate with engineering teams on deployment, promote data-driven decision-making, and mentor colleagues.

Build, validate, deploy, monitor, and maintain predictive models and analytical reports. Translate findings for technical and non-technical stakeholders, promote data literacy, deliver training, collaborate on data infrastructure, apply advanced statistical and machine learning methods, and support analytical requests across mental health and crisis care programs.

Consults with business stakeholders to identify problems, gather and prepare data, perform statistical analysis, build models and forecasts, and communicate findings. Develops data-driven recommendations, reusable data assets, dashboards, documentation, and reporting processes. Manages multiple projects, supports process improvements, ensures data quality, and provides peer coaching.

Leads and mentors data scientists while owning biomedical data science projects from analytical planning through validated delivery. Coordinates project plans, reviews, dependencies, timelines, and cross-functional collaboration. Translates scientific, clinical, product, and commercial questions into analytical solutions; reviews statistical and machine-learning methods, code, validation, and model artifacts. Ensures reproducibility, documentation, quality, and scientific rigor while communicating results to technical and nontechnical stakeholders.