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 :

- 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

Ideal Candidate :

- Must have 4+ years of experience as an analytics consultant with atleast 2 years in pharma domain

- Must have hands-on experience working with patient-level datasets (claims, EHR, lab, pharmacy data)

- Must have worked on patient journey analysis, treatment patterns, disease progression and advanced analytics

- Must have experience with SQL, Python and Predictive modelling (regression, classification, clustering)

- Must have experience combining multiple healthcare datasets and building longitudinal patient views

- Ability to translate complex analysis into actionable business/clinical insights

- Must have experience with time-series analysis and/or survival analysis - specifically to study treatment duration, patient drop-off, or retention trends

- Must have experience building risk stratification models using ML techniques to prioritize patients based on clinical or behavioural risk factors