Top Healthtech Data Science Jobs

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.

Design and deploy generative AI workflows and multi-agent architectures for healthcare applications. Curate EHR and claims data, extract clinical variables from unstructured medical text, and develop evaluation pipelines for agent safety, accuracy, factuality, and compliance. Apply advanced machine learning, NLP, and LLM techniques to create trustworthy longitudinal datasets supporting autonomous care agents and clinical decision tools. Communicate technical findings to technical and non-technical stakeholders.

Leads projects in data science to analyze complex data sets using advanced statistical methods, providing insights for business decisions and managing cross-functional teams.

Own end-to-end data science and AI evaluation projects, developing frameworks, metrics, golden datasets, benchmarks, pipelines, and dashboards for LLM- and speech-based systems. Analyze failure modes, calibrate LLM judges against human ground truth, and drive continuous product improvement. Collaborate with AI/ML engineers and product managers, contribute to technical strategy, mentor junior data scientists, and establish evaluation best practices.

Lead the design, development, deployment, and governance of advanced analytics, machine learning, and AI solutions using large healthcare datasets. Responsibilities include data preparation, statistical and predictive modeling, model monitoring, explainability, visualization, stakeholder communication, and translating findings into clinical and operational improvements. The role also mentors data professionals, advises leaders, manages initiative priorities and risks, and promotes responsible AI and Agile practices.

Architects data models and pipelines that transform multimodal surgical and robotic data into research-ready AI datasets. Owns data collection strategy, quality frameworks, validation, anomaly detection, lineage, annotation workflows, and dataset tooling. Partners with AI researchers, surgeons, clinicians, and robotics engineers to capture relevant signals and support intelligent surgical system development. Contributes to embodied AI infrastructure and data strategy.

Develop machine learning and AI models using clinical, claims, and social determinants of health data to improve pediatric patient outcomes. Partner with stakeholders and cross-functional teams to define requirements, optimize data collection, build data assets, conduct exploratory analysis, and deliver strategic and patient-care insights. Query Snowflake independently and present findings to nontechnical staff.