Senior Analyst - Data Science

Role: Senior Analyst - Data Science Descriptions: We are looking for a results-driven and hands-on Lead Data Scientist / Analyst 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 Decision Engine frameworks, implementing advanced analytics solutions, and mentoring junior team members. Key Responsibilities: • Partner with cross-functional teams and client stakeholders to gather business requirements and translate them into robust ML/analytical solutions. • Design and implement Decision Engine workflows to support Next Best Action (NBA) recommendations in omnichannel engagement strategies. • Analyze large and complex datasets across sources like APLD, sales, CRM, call plans, market share, patient claims, and segmentation data. • Perform ad hoc and deep-dive analyses to address critical business questions across commercial and medical teams. • Develop, validate, and maintain predictive models for use cases such as patient journey analytics, HCP targeting, sales forecasting, risk scoring, and marketing mix modeling. • Implement MLOps pipelines using Dataiku, Git, and AWS services to support scalable and repeatable deployment of analytics models. • Ensure data quality through systematic QC checks, test case creation, and validation frameworks. • Lead and mentor junior analysts and data scientists in coding best practices, feature engineering, model interpretability, and cloud-based workflows. • Stay up to date with industry trends, regulatory compliance, and emerging data science techniques relevant to life sciences analytics. • 5+ years of hands-on experience in pharmaceutical commercial analytics, with exposure to cross-functional brand analytics, omnichannel measurement, and ML modeling. • At least 3 years of experience developing and deploying predictive models and ML pipelines in real-world settings. • 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. • Familiarity with dashboarding tools like Tableau or PowerBI is a plus. • Strong communication and documentation skills to effectively convey findings to both technical and non-technical audiences. • Ability to work in a dynamic, fast-paced environment and deliver results under tight timelines.