Data Scientist - Patient Analytics
Summary
Senior Data Scientist at DataZymes in Bengaluru doing patient-level healthcare analytics: integrating claims, EHR, lab, and pharmacy datasets to map patient journeys, run treatment-pattern and line-of-therapy analyses, and build predictive models in Python and SQL, then translating findings into executive-ready insights for clients.
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