Junior Data Scientist

Role & Responsibilities

DataZymes is seeking a highly analytical and client-focused Data Scientist with 2 to 4 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.

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 Modeling-

• 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 modeling approaches
• Adapt to evolving client priorities and ambiguous problem statements

Ideal Candidate

• Strong Pharma Analytics Profile
• Mandatory (Experience) : Must have 2+ years of experience as an analytics consultant with atleast 1 year 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) : Masters 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