Senior Data Scientist - (PharmaTech/life Sciences)

Role & Responsibilities

Our client 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