Data Science Consulting

Overview

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.

• 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 oversigh

• 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.