● Manage a Hybrid Inference Roadmap: Develop and execute a strategy for core prediction models that serves two distinct goals:

○ Productization into our core platform for the most high-usage use cases, and
○ Tech-enabled-Service for internal teams to deliver bespoke predictions to customers. You will ruthlessly prioritize to ensure the core inference engine evolves to support both, while minimizing forking.

● Navigate Internal Complexity: Act as a diplomat and strategist within the organization. You will bring clarity to complex, sometimes conflicting internal priorities, driving alignment between Engineering, Science, Go-to-Market, and Product leadership.
● Drive External Partnerships: Serve as the primary product interface for key partners helping to build and consume our predictions. You will translate partner scientific requirements into model capabilities, manage expectations around prediction accuracy/confidence, and ensure our technology creates value for them while remaining scalable for us.
● Bridge the Gap (Science <> ML): Translate preclinical business objectives into technical and data requirements. You will ensure that a single inference engine can effectively serve diverse use cases across the drug discovery pipeline (e.g., Target Identification vs. Safety Assessment vs. Study Design).
● Measure Impact: Define and report on metrics that capture the value of the platform, moving beyond just model performance (F1, Precision/Recall) to demonstrate business ROI and scientific utility to diverse internal stakeholders.

● 5+ years, preferably 7+ years, as a Product Manager, with increasing responsibility on AI/ML or Data Science product
● Biopharma/Preclinical Fluency: You understand the drug discovery lifecycle (from Target ID to Lead Optimization). You can speak the language of scientists to understand the nuance of how a "prediction" helps or hinders their specific workflow. 2+ years experience working in the Biotech or Pharma industry is a strong plus, where you can bring a deep understanding of pre-clinical R&D to this role.
● Experience with Probabilistic Products: You have successfully managed products where the core value is a prediction or inference. You know how to handle user expectations around uncertainty, confidence scores, and false positives/negatives in high-stakes environments.
● Experience in Technology-as-a-Service: A proven track record managing inference technology that is consumed via API or service layer, where that same technology powers internal user-facing products.
● Exceptional Stakeholder Management: You excel at navigating internal politics and organizational dynamics. You have a history of driving consensus among strong-minded research scientists and engineers without having direct authority.
● Technical Fluency: Familiarity with LLMs, Knowledge Graphs, or predictive modeling. You don't need to write code, but you must be able to debate trade-offs (e.g., latency vs. accuracy, generalization vs. specificity) with engineering leads.
● Strategic Resilience: You are comfortable with ambiguity and can make data-informed decisions even when the path forward isn't obvious.