About this episode
Jeremy Forman joins us to open up about the hurdles– and successes that come with building data products for pharmaceutical companies. Although he’s new to Pfizer, Jeremy has years of experience leading data teams at organizations like Seagen and the Bill and Melinda Gates Foundation. He currently serves in a more specialized role in Pfizer’s R&D department, building AI and analytical data products for scientists and researchers. .
Jeremy gave us a good luck at his team makeup, and in particular, how his data product analysts and UX designers work with pharmaceutical scientists and domain experts to build data-driven solutions.. We talked a good deal about how and when UX design plays a role in Pfizer’s data products, including a GenAI-based application they recently launched internally.
Highlights/ Skip to:
(1:26) Jeremy's background in analytics and transition into working for Pfizer
(2:42) Building an effective AI analytics and data team for pharma R&D
(5:20) How Pfizer finds data products managers
(8:03) Jeremy's philosophy behind building data products and how he adapts it to Pfizer
(12:32) The moment Jeremy heard a Pfizer end-user use product management research language and why it mattered
(13:55) How Jeremy's technical team members work with UX designers
(18:00) The challenges that come with producing data products in the medical field
(23:02) How to justify spending the budget on UX design for data products
(24:59) The results we've seen having UX design work on AI / GenAI products
(25:53) What Jeremy learned at the Bill & Melinda Gates Foundation with regards to UX and its impact on him now
(28:22) Managing the "rough dance" between data science and UX
(33:22) Breaking down Jeremy's GenAI application demo from CDIOQ
(36:02) What would Jeremy prioritize right now if his team got additional funding
(38:48) Advice Jeremy would have given himself 10 years ago
(40:46) Where you can find more from Jeremy
Quotes from Today’s Episode
“We have stream-aligned squads focused on specific areas such as regulatory, safety and quality, or oncology research. That’s so we can create functional career pathing and limit context switching and fragmentation. They can become experts in their particular area and build a culture within that small team. It’s difficult to build good [pharma] data products. You need to understand the domain you’re
