Shape strategy and execution for clients in the Life Sciences sector by leveraging pharmaceutical commercial, real-world, and digital data. This role demands a blend of deep life sciences expertise, analytics leadership, and proficiency in AI and modern data platforms. You will be instrumental in defining complex business challenges, designing effective data solutions, leading multidisciplinary teams, and translating insights into actionable strategies for brand management, customer engagement, field effectiveness, and patient analytics. The ideal candidate is a trusted advisor, adept at connecting market dynamics with data and technology to deliver scalable, compliant, and impactful business solutions.

Lead client engagements focused on commercial analytics, including market opportunity assessment, patient journey mapping, segmentation, omnichannel performance, and launch readiness. Frame ambiguous client issues into clear hypotheses and analytical work plans. Guide the selection and interpretation of pharmaceutical data, ensuring appropriate use and understanding of limitations. Drive the development and validation of AI and advanced analytics solutions, such as segmentation, propensity modeling, next-best-action, and NLP, while ensuring responsible AI practices. Provide direction for data architecture, including scalable analytical datasets and robust ETL/ELT pipelines. Act as a trusted advisor to commercial, marketing, and sales stakeholders, synthesizing findings into strategic recommendations. Lead, coach, and develop multidisciplinary teams, contributing to practice growth through proposals, thought leadership, and offering development.

A minimum of 7-12 years of experience in life sciences or healthcare analytics consulting, with a proven track record in client and team leadership. Essential skills include deep familiarity with pharmaceutical datasets (e.g., IQVIA, Komodo) and a strong grasp of the US pharmaceutical commercial model. Demonstrated leadership across key commercial use cases and experience shaping analytics solutions using SQL, Python/R, BI tools, and cloud platforms are required. Technical depth to review solution designs and direct data integration efforts, including analytical data models and ETL/ELT pipelines, is crucial. Experience designing or governing ML/generative AI solutions with a focus on responsible AI is also necessary. Strong executive communication, strategic problem-solving abilities, and commercial acumen are vital for success in this role.