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 sales/Rx, CRM, call plans, market share 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 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 4+ 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

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Analyst - Data Science