Job Description
Must Have
Role: Senior Analyst - Data Science
Description: We are looking for a results-driven and hands-on Senior Data Scientist with 5-6 years of experience to lead analytical solutioning and model development in the pharmaceutical commercial analytics domain. The ideal candidate will play a central role in designing and deploying test-and-learn campaign impact measurement approaches using statistics & advanced analytics solutions, and mentoring junior team members.
Key Responsibilities:
1. Experiment Design & Methodology:
• Design and operationalize A/B tests, multivariate tests, and quasi‑experimental designs across pharma omnichannel campaigns (HCP digital, email, rep detailing, remote engagement, media).
• Define test hypotheses, success criteria, treatment/control structures, and eligibility rules aligned to brand and business objectives.
• Select and apply appropriate causal inference methods (e.g., randomized control, propensity score matching, diff‑in‑diff) when true randomization is not feasible.
• Ensure experiments comply with pharma‑specific constraints (no patient‑level targeting, rep allocation rules, ethical considerations).
2. Analytics, Modeling & Statistical Rigor
• Conduct statistical analysis of experiment results, including power analysis, confidence intervals, significance testing, and effect size estimation.
• Build and maintain campaign effectiveness models leveraging regression, Bayesian methods, or uplift modeling as needed.
• Interpret results with a focus on business relevance, not just statistical significance.
• Validate assumptions, handle biases, and document methodological limitations clearly.
3. Insights Generation & Storytelling
• Translate complex analytical findings into clear, action‑oriented insights for brand teams, omnichannel leads, and commercial leadership.
• Create experiment read‑outs, test scorecards, and executive‑ready summaries focused on ROI and optimization levers.
• Recommend next‑best actions (scale, optimize, stop, retest) based on evidence.
• Contribute to codifying test‑and‑learn best practices across brands and markets.
4. Stakeholder Collaboration & Consulting Mindset
a. Act as an analytics partner to omnichannel, marketing, digital, and field operations teams.
b. Support campaign planning by advising on “what to test”, expected lift ranges, and measurement feasibility.
c. Educate non‑technical stakeholders on experiment interpretation, caveats, and confidence levels.
Ideal Profile:
• 5+ years of hands-on experience in pharmaceutical commercial / omnichannel analytics, with exposure to cross-functional brand analytics, omnichannel measurement, and ML modeling.
• Proven experience with data platforms such as Snowflake, Dataiku, AWS, and proficiency in PySpark, Python, and SQL.
• Experience with MLOps practices, including version control, model monitoring, and automation.
• Strong understanding of pharmaceutical data assets (e.g., APLD, DDD, NBRx, TRx, specialty pharmacy, CRM, digital engagement).
• Proficiency in ML algorithms (e.g., XGBoost, Random Forest, SVM, Logistic Regression, Neural Networks, NLP).
• Experience in key use cases: Next Best Action, Recommendation Engines, Attribution Models, Segmentation, Marketing ROI, Collaborative Filtering.
• Hands-on expertise in building explainable ML models and using tools for model monitoring and retraining.
• Strong communication and documentation skills to effectively convey findings to both technical and non-technical audiences.