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Tools in this posting

Excel

Source

- Strong understanding of key pharma datasets such as LAAD and other secondary data sources. - Proficiency in building and managing Excel-based forecasting models; exposure to advanced forecasting tools is a plus. - Excellent problem-solving skills with the ability to synthesize insights from complex data sets.

Job description

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About ProcDNA
ProcDNA is a global rocket ship in life sciences consulting. We fuse design thinking with cutting-edge tech to create game-changing Commercial Analytics and Technology solutions for our clients. We're a passionate team of 450+ across 9 offices, all growing and learning together since our launch during the pandemic. Here, you won't be stuck in a cubicle - you'll be out in the open water, shaping the future with brilliant minds. Ready to join our epic growth journey?
What we are looking for
We are seeking an Engagement Lead-Analytics to join our team—someone with a strong understanding of the pharmaceutical industry and detailed knowledge of datasets such as LAAD, DDD, and XPO. You thrive on working with complex data sets to solve real-world problems and can translate insights into actionable business strategies. In this role, you will be responsible for driving business performance, identifying and navigating risks, and developing pragmatic, data-driven strategies that deliver tangible impact.
What you will do

Lead forecasting engagements for pharma clients, driving business performance through robust, data-driven insights.
Develop and manage forecasting models for in-line and pipeline assets across multiple therapy areas.
Translate complex datasets and analytical findings into actionable business strategies and recommendations. Collaborate with cross-functional client and internal teams to align on assumptions, scenarios, and methodologies.
Identify potential risks and opportunities impacting forecasts and deliver solutions to support decision-making. Contribute to building internal assets and forecasting accelerators

Must Have

5–7 years of hands-on experience in pharmaceutical forecasting (inline and pipeline assets).
Strong understanding of key pharma datasets such as LAAD and other secondary data sources.
Proficiency in building and managing Excel-based forecasting models; exposure to advanced forecasting tools is a plus.
Excellent problem-solving skills with the ability to synthesize insights from complex data sets.
Strong communication and client management skills, with experience presenting insights to senior stakeholders.