Platform Product Manager | Pharma AI Platforms

Job Description

Platform Product Manager Pharma AI Platforms

We are looking for a Platform Product Manager who can identify, conceptualize and build the next generation of AI-enabled platforms for the pharmaceutical industry.

This is a highly research-oriented product role requiring someone who can understand complex pharmaceutical problems, investigate how those problems are currently solved, identify gaps in existing approaches, and translate those insights into differentiated platform products.

The role spans the Commercial, Clinical and Regulatory domains and requires the ability to connect diverse sources of pharmaceutical information in meaningful ways.

The ideal candidate will have strong credibility in understanding both formal pharmaceutical datasets and internet-based information sources and, more importantly, understand how these sources can be joined, contextualized and transformed into actionable intelligence.

For example, the candidate should be comfortable thinking about how clinical trials, scientific publications, regulatory filings, product labels, HCP information, commercial datasets, claims/RWD, conference information and other external sources can be brought together to answer questions that are difficult to solve using any individual dataset.

You will work closely with pharmaceutical business leaders, subject matter experts, data scientists, AI engineers and product engineering teams to take ideas from research problem definition product hypothesis platform capability customer validation scalable product.

This role is particularly suited to someone who enjoys operating in an environment where the problems are not always well defined and where significant value comes from discovering what should be built, rather than simply delivering what has already been specified.

Responsibilities

1. Identify High-Value Pharma Problems

• Research and identify important, unresolved problems across Commercial, Clinical and Regulatory functions.
• Understand how pharmaceutical organizations currently approach these problems and identify limitations in existing processes, data and technology.
• Distinguish between incremental automation opportunities and problems that warrant a new platform capability.
• Develop hypotheses around where AI, data and intelligent workflows can create significant business value.

1. Conduct Industry & Technology Research

• Continuously track developments across pharma, biotechnology, healthcare data and AI.
• Research emerging datasets, technologies, methodologies and business models relevant to pharmaceutical organizations.
• Monitor scientific literature, clinical trials, regulatory developments, conference publications, industry reports, competitor products and emerging AI capabilities.
• Convert research findings into potential product opportunities and strategic recommendations.

1. Build Pharma Data & Knowledge Strategies

• Develop a strong understanding of both formal and non-traditional pharmaceutical data sources.
• Evaluate how structured, semi-structured and unstructured datasets can be combined to solve specific business problems.
• Explore relationships between sources such as clinical trials, regulatory documents, scientific literature, commercial data, HCP data, RWD and public internet sources.
• Define data acquisition, enrichment, normalization, semantic and knowledge strategies required to support platform products.
• Identify opportunities to create proprietary data assets and differentiated knowledge layers.

1. Define Platform Product Strategy

• Translate identified problems into product visions, hypotheses and platform strategies.
• Define reusable capabilities that can support multiple pharmaceutical use cases and customers.
• Develop product roadmaps balancing immediate customer value with longer-term platform differentiation.
• Define product requirements, user journeys, workflows, data requirements and success metrics.
• Identify opportunities for common platform capabilities across Commercial, Clinical and Regulatory use cases.

1. Drive AI-Native Product Development

• Work with AI and engineering teams to determine where technologies such as LLMs, RAG, knowledge graphs, agents, predictive models and intelligent workflows can create differentiated value.
• Define appropriate human-in-the-loop, validation, explainability and governance mechanisms.
• Establish product-level requirements for AI quality, accu…