Top Healthtech Data Science Jobs

Supports data preparation, statistical and machine learning model development, data pipeline tasks, deployment monitoring, documentation, and stakeholder collaboration. The intern applies Python, SQL, statistics, machine learning, and version control while learning production-grade data science practices in healthcare and supply chain contexts.

Leads Business Analysts and Product Managers responsible for Optum Insight’s data mart and financial technology products. Defines product vision, roadmaps, requirements, backlogs, and delivery priorities; oversees testing, data tie-outs, automated SOX controls, and UAT readiness. Partners with technology teams and executive stakeholders to advance financial systems transformation, modern integrations, and data-driven decision-making.

Lead patient lifecycle analytics for a digital health product. Build production-ready churn, resurrection, and targeting models; design experiments and apply causal inference; deliver actionable analyses with quantified opportunities; define product metrics; and contribute to the company-wide experimentation platform. Partner closely with Product, Engineering, Design, and Growth teams to shape priorities and improve patient outcomes.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Leverage healthcare platform data to identify behavioral patterns, develop product and client-facing analytics, and guide data strategy. Lead data projects from ingestion through analysis and recommendations while collaborating with product managers, analysts, and developers. Apply advanced statistics, SQL, Python, distributed processing, machine learning, and data visualization to communicate actionable insights to stakeholders.

Develop advanced algorithms and apply statistical, data mining, machine learning, and AI techniques to large structured and unstructured datasets. Identify trends, evaluate emerging data technologies, and communicate analytical findings to clients and ZS stakeholders. The role requires collaboration across multiple projects, strong quantitative and research skills, and proficiency with Python, SQL, Pandas, and PySpark. Client-facing work may require travel.

Lead and build a greenfield marketing measurement function across B2C and B2B. Own attribution, incrementality, MMM, MTA, LTV, and experimentation; hire and mentor data scientists; partner with marketing leadership to turn statistical results into investment decisions; integrate measurement with data platform and GenAI tools.

Build predictive models for churn, expansion, and look-alikes; run marketing mix and incrementality analyses; create leading indicators and cohort reporting; partner with Sales, Marketing, and RevOps to translate analytics into actionable recommendations and inform data infrastructure needs.

Lead and grow a data science team focused on provider experience. Define strategy, build roadmaps, run analyses, experiments, and predictive models, surface insights, and partner cross-functionally to drive measurable improvements in provider engagement and product experience.

Leads the strategy for scaling data science analyses used in pharmaceutical asset acquisition and diligence. Directs cross-functional Data Science and Engineering teams, determines which one-off analyses should become landscape analyses or durable tools, sets roadmaps, evaluates statistical and technical validity, identifies AI opportunities, and connects scientific findings to business impact, asset valuation, and sourcing decisions.