Note: The job is a remote job and is open to candidates in USA. Caliper is a healthcare technology company focused on value-based care risk analytics. The Data Science Manager will lead the development, validation, deployment, and continuous improvement of statistical and machine-learning capabilities, while evaluating healthcare interventions and translating complex data into actionable insights. The role partners with clinical, product, engineering, and operational stakeholders to build rigorous, explainable, scalable solutions that influence care and financial outcomes.
Responsibilities
- Lead the design, development, validation, deployment, and monitoring of statistical and machine-learning models across Caliper’s value-based care analytics platform
- Develop and improve methodologies for risk stratification, impactability modeling, intervention evaluation, utilization prediction, and population health analytics
- Analyze healthcare data including claims, EHR, care-management, SDoH, quality, and unstructured clinical data to generate actionable patient- and population-level insights
- Establish best practices for experimentation, causal analysis, model validation, explainability, data quality, reproducibility, and analytical governance
- Partner with product and engineering teams to translate clinical and business needs into scalable analytical solutions and production-ready models
- Work closely with clinical and operational stakeholders to ensure analytical outputs are clinically meaningful, practical, and aligned with real-world care delivery
- Communicate complex findings, assumptions, limitations, and recommendations clearly to clinical, technical, executive, payer, and other non-technical stakeholders
- Identify opportunities to expand Caliper’s AI and data science capabilities and help shape the long-term analytics and product roadmap
Skills
- 5+ years of progressively responsible experience in data science, applied statistics, machine learning, healthcare analytics, or a closely related quantitative discipline
- Experience working in an environment where analytical outputs influence meaningful operational, financial, clinical, or product decisions
- Deep experience analyzing medical claims and longitudinal healthcare utilization with specific experience working directly with Federally Qualified Health Centers (FQHCs), Community Health Centers (CHCs), Primary Care Associations (PCAs), safety-net providers, or similar organizations
- Excellent written and verbal communication skills with demonstrated ability to explain statistical and machine-learning concepts to non-technical and executive audiences
- Excellent foundation in statistics and statistical modeling, both theoretically and in practical application with ability to frame ambiguous business or clinical questions as rigorous analytical problems
- Advanced experience developing predictive models using modern data-science and machine-learning techniques, including moving models beyond exploratory analysis into operational or production environments
- Strong programming skills in Python and/or R for statistical analysis, modeling, experimentation, and data science
- Proficiency with SQL and working with large relational or analytical datasets
- Experience working with data visualization and analytical reporting tools
- Familiarity with software-development practices, versioning, CI/CD
- Meaningful experience working with healthcare data, healthcare analytics, value-based care, population health, health economics, clinical analytics, or a closely related domain
- Medical or pharmacy claims data, EHR data, Care-management or SDoH dataClinical quality data
- Experience communicating analytical results to product clients and other external stakeholders, particularly clinical or healthcare-operational professionals
- Bachelor's or Master's degree in Statistics, Data Science, Computer Science, Applied Mathematics, Biostatistics, Health Informatics, Economics, Operations Research, Engineering, or another quantitatively rigorous discipline; or equivalent practical experience demonstrating comparable quantitative expertise
- Experience evaluating healthcare savings, utilization reductions, or intervention ROI in a manner capable of supporting payer, actuarial, or financial review
- Exposure to NLP, large language models, clinical text analytics, or other methods for extracting structured information from unstructured clinical data
- Experience working in an early-stage or high-growth healthcare technology company
Company Overview
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