Data Science Consulting

We are looking for a Data Science Consultant with strong expertise in the Pharmaceutical or Life Sciences domain to drive data-driven decision-making for global pharma clients. This role focuses on problem-solving, stakeholder engagement, and translating business needs into analytics solutions, rather than deep hands-on model development. The ideal candidate will act as a bridge between business stakeholders and analytics teams, guiding analytical approaches, interpreting outputs, and delivering actionable insights. This role requires strong domain understanding, consulting mindset, and storytelling ability to influence decision-making.Roles & Responsibilities Work closely with client stakeholders to understand business challenges and translate them into structured analytical approaches. Define and guide analytics use cases such as patient journey, forecasting, segmentation, promotional effectiveness, and market access insights. Collaborate with data science and engineering teams to drive solution development and ensure alignment with business goals. Interpret analytical outputs and translate them into clear, actionable insights and strategic recommendations. Present insights to senior stakeholders and support decision-making through data-driven storytelling. Ensure analytics solutions are aligned with pharma business processes and domain context. Drive client engagement, requirement gathering, and solution discussions in a consulting setup. Contribute to problem structuring, solution design, and delivery oversighSkills Required Strong consulting mindset with the ability to translate business problems into analytical solutions. Excellent stakeholder management and communication skills, especially in client-facing roles. Strong storytelling and presentation skills to convey insights to senior leadership. Ability to work in cross-functional teams and manage ambiguity effectively Experience in the pharmaceutical or life sciences industry is required. Familiarity with use cases such as commercial analytics, patient analytics, market access, or brand analytics. Understanding of pharma data sources such as claims, EMR/EHR, Rx, and market access datasets (IQVIA, Symphony, MMIT, etc.). Basic understanding of data science and analytics concepts (statistics, modeling approaches, etc.). Familiarity with tools such as Python, SQL, or visualization tools is a plus, but not mandatory.