Data Scientist - Patient Analytics

We are looking for passionate and driven professionals to join DataZymes, a next-generation analytics and data science company founded in 2016. At DataZymes, we focus on driving technology-led innovation and helping clients maximize the value of their data and analytics investments through cutting-edge platforms and consulting expertise. If you are excited about working on impactful solutions in the healthcare analytics space and want to be part of a high-performance, fast-growing team, we’d love to hear from you. DataZymes is seeking a highly analytical and client-focused Senior Data Scientist with 4–7 years of experience in healthcare data analytics. The ideal candidate will combine deep clinical understanding, strong patient-level data expertise, and advanced analytical skills to generate insights that drive strategic and operational decisions. This role requires hands-on experience with integrated healthcare datasets (claims, EHR, lab, pharmacy) and the ability to translate complex analyses into clear, actionable business recommendations Clinical & Therapeutic Analytics: Apply strong understanding of healthcare delivery models and patient care pathways Conduct patient centric analysis like treatment pattern, line-of-therapy, and disease progression analyses etc. Patient-Level Data Integration & Journey Mapping Integrate and analyze claims, EHR, lab, and pharmacy datasets etc to develop longitudinal patient journeys across multiple care settings Define cohorts, enrollment logic, and episode-of-care frameworks Ensure data quality, consistency, and reproducibility Advanced Analytics & Predictive Modeling Develop complex SQL /Python queries for large-scale healthcare datasets Use Python to perform predictive modelling to build and validate models (classification, regression, survival, clustering etc) Data Interpretation & Storytelling Translate analytical findings into clear, strategic insights and develop executive-ready presentations and dashboards Communicate complex methodologies to both technical and non-technical stakeholders Quantify business and clinical impact of recommendations Innovation & Learning Agility Quickly ramp up in new therapeutic areas and problem domains Test innovative analytical methods and modeling approaches Adapt to evolving client priorities and ambiguous problem statements