Scientific Product Director, Clinical Prediction

About this role

About Orakl Oncology At Orakl Oncology, we are accelerating the development of oncology treatments. Today, fewer than 5% of new cancer drugs succeed in clinical trials. Clearly, new methods are needed. We combine cutting-edge biology and AI to build the next generation of insight platforms with the world's largest cohort of patient tumor avatars. These avatars fuel our AI-powered predictive engine, helping to anticipate clinical trial outcomes, validate therapies, and uncover new drug candidates.

By generating multimodal, real-world data, we deliver insights that consistently outperform existing solutions. Our mission is simple yet ambitious: to bring more effective treatments to patients who need them - and to make drug development smarter, faster, and more personalized. We collaborate with top hospitals, research institutes, and pharmaceutical companies worldwide. Backed by leading investors, we are a fast-growing, mission-driven startup at the intersection of science and technology.

The role Our clinical-prediction offer gives pharma partners the evidence to guide decisions before and during clinical development. We are looking for a Scientific Product Director to turn this science into sellable products: decide what gets developed and when, define what each capability can claim and output, and shape how results are delivered to partners. You will report to Gustave Ronteix, CTO and co-founder, and work daily with a tight-knit interdisciplinary team composed of Computational Biology, Data Science, R&D, Platform Strategy & Operations, BD and Pharma Partnerships.

What you'll do 1. Own the product roadmap Design and build a scalable product capable of derisking and accelerating clinical development of drug candidates based on Orakl’s scientific capabilities Turn signals from BD, Pharma Partnerships and R&D into capability definitions: specs, target metrics, validation plan, in- vs out-of-scope definition, cost to deliver Set priorities and dates with Engineering and Data Science; flag slippage early and make the trade-offs Identify productization-gaps from delivery audits and analysis, and feed recurring ones back into the roadmap 2.