Role & Responsibilities
We are 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
Ideal Candidate
- Strong Pharma Analytics Profile
- Mandatory (Experience) : Must have 4+ years of experience as an analytics consultant with atleast 2 years in pharma domain
- Mandatory (Skill 1) : Must have hands-on experience working with patient-level datasets (claims, EHR, lab, pharmacy data)
- Mandatory (Skill 2) : Must have worked on patient journey analysis, treatment patterns, disease progression and advanced analytics
- Mandatory (Skill 3) : Must have experience with SQL, Python and Predictive modelling (regression, classification, clustering)
- Mandatory (Skill 4) : Must have experience combining multiple healthcare datasets and building longitudinal patient views
- Mandatory (Skill 5) : Ability to translate complex analysis into actionable business/clinical insights
- Mandatory (Skill 6): Must have experience with time-series analysis and/or survival analysis - specifically to study treatment duration, patient drop-off, or retention trends
- Mandatory (Skill 7): Must have experience building risk stratification models using ML techniques to prioritize patients based on clinical or behavioural risk factors
- Mandatory (Company) : PharmaTech/life sciences companies
- Mandatory (Note 1) : Hybrid, WFH flexibility 6 days a month
- Mandatory (Note 2) : CTC is inclusive of 10% variable
- Preferred (Education) : Master's degree
- Preferred (Experience) : Experience delivering analytics in a client-facing environment
- Preferred (Skill): Experience using NLP on unstructured patient data (clinical notes, call logs) to identify barriers like side effects, cost issues, or therapy fatigue
Skills: data,healthcare,datasets,analytics,models
