Top Healthtech Data Science Jobs

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Lead Chewy Ads Data Science across onsite and offsite channels, owning strategy, roadmap, models, and experimentation. Build and mentor a team to deliver production ML systems for ranking, bidding, budget optimization, ROAS, and GenAI-powered advertiser tooling while partnering with Product and Engineering and representing science to senior leadership.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Use statistical and computational methods to analyze large healthcare datasets, develop and validate models, produce visualizations and metrics, and translate results for non-technical senior leaders. Establish scalable analysis pipelines, monitor performance indicators, manage multiple projects, and contribute to technical reports and publications.

Develop and implement quantum and hybrid algorithms for biomedical research, including quantum chemistry and electronic-structure calculations. Analyze and curate complex clinical and biological datasets using statistical, mathematical, predictive modeling, and programming techniques. Support data validation, hypothesis testing, predictive modeling, reporting, process improvement, and research presentations while assisting the Principal Investigator.

Develop deep learning and AI/ML models to predict cardiac events from ECG and physiological signals. Apply signal processing, analyze implantable-device datasets, build Python tools, validate algorithms, and support feasibility studies for diagnostic features. Collaborate with clinical, software, and hardware teams to improve implantable-device performance and patient outcomes.

Develop and deploy production-grade AI and data science solutions for scientific discovery, including LLM applications, NLP, semantic search, RAG, agentic workflows, knowledge graphs, and recommendation systems. Lead model evaluation, experimentation, monitoring, and lifecycle management while collaborating with engineering, product, research, and domain teams. Influence technical strategy, mentor data scientists, and deliver reliable, scalable, evidence-grounded AI products.

Develop machine learning and deep learning pipelines for 3D timelapse microscopy image analysis. Responsibilities include preprocessing large datasets, building and optimizing segmentation, classification, and feature-extraction models, scaling image-analysis workflows on HPC infrastructure, maintaining reproducible code and records, participating in reviews, and collaborating with experimental scientists in a multidisciplinary research environment.

Leads the strategic vision, organizational direction, and delivery of advanced data science programs. Manages multiple teams of data scientists and analysts, including managers, while overseeing statistical modeling, machine learning, data pipelines, and analytical frameworks. Partners with executive stakeholders to align initiatives with enterprise goals, advocates for resources, develops talent, ensures regulatory compliance, and promotes innovation and ethical data practices.

Build and validate production machine learning solutions for MedTech using Databricks, Python, structured and unstructured data. Responsibilities include data pipelines, feature engineering, model development, evaluation, regression testing, explainability, model registration, deployment, monitoring, drift detection, troubleshooting, and regulatory documentation. The role also integrates AI models through APIs and agentic frameworks and collaborates with commercial, technology, and business stakeholders.