Senior Data Scientist - Patient Analytics

About Us:

Founded in 2016, DataZymes is a next-generation analytics and data science company driving technology and digital-led innovation for our clients, thus helping them get more value from their data and analytics investments. Our platforms are built on best-of-breed technologies, thus protecting current investments while providing clients more bang for their buck. As we are a premier partner for many Analytics and Information Management companies, we also provide advisory and consulting services to clients helping them make the right decisions and put together a long-term roadmap.

Our mission at DataZymes is to scale analytics and enable healthcare organizations in achieving non- linear, long term and sustainable growth. In a short span, we have built a high-performance team in focused practice areas, built digital-enabled solutions, and are working with some marquee names in the US healthcare industry.

Job Location: Bengaluru

Experience Required: 4-7 Years

Industry: Pharmaceutical / Life Sciences

Role Overview:

DataZymes is seeking a highly analytical and client-focused Data Scientist with 4-7 years of experience in Patient-level data. 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.

Key Responsibilities:

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, enrolment logic, and episode-of-care frameworks

Ensure data quality, consistency, and reproducibility

Advanced Analytics & Predictive Modelling:

Develop complex SQL /Python queries for large-scale healthcare datasets

Developed risk stratification models using machine learning techniques to prioritize patients based on clinical and behavioural risk factors.

Applied time-series and survival analysis to study treatment duration, drop-offs, and patient retention trends.

Leveraged NLP on patient interaction data (notes, call logs) to identify common barriers like side effects, cost issues, and therapy fatigue

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:

Innovation & Learning Agility

Quickly ramp up in new therapeutic areas and problem domains

Test innovative analytical methods and modelling approaches

Adapt to evolving client priorities and ambiguous problem statements