Senior Product Manager, Deep Genomics

• 5+ years Product Management experience, including ownership of complex, technical internal platforms
• Hands‑on understanding of ML concepts—either through model building (as ML/Data Scientist) or prior Product Management experience building ML tooling
• Proven track record defining and executing on product strategy and roadmaps that drive measurable business value — especially in complex problem spaces
• Excellent communication skills and experience collaborating with a diverse set of stakeholders — including technical teams and senior management
• Experience working with cross-functional teams in an agile environment
• Previous management experience and/or interest in managing a team
• Natural curiosity and a passion for learning

Desirable

• As a Senior Product Manager on the ML Platform team, you will define and drive the vision, strategy, and roadmap for the machine learning infrastructure that powers Deep Genomics’ drug discovery engine
• Your work will support both the development and application of machine learning models across a wide range of therapeutic research initiatives
• You’ll work closely with two key customer groups:
• Model Builders: ML Scientists, Bioinformaticians, and Data Scientists who develop and iterate on internal and state-of-the-art external models. They depend on the ML Platform for robust tools to train, evaluate, deploy, and share models — with a strong emphasis on reproducibility, scalability, and experimentation speed
• Model Users: Team members applying ML models to accelerate their work in areas such as Target Identification, Molecule Design, and Molecule Optimization.
• This includes both computational users (e.g., ML Scientists, Bioinformaticians) who work directly with models, and non-computational users (e.g., Target Curation Scientists, Experimental Biologists, Leadership) who need accessible tools to explore and act on model predictions
• You’ll partner closely with teams across Engineering, Product Management, Machine Learning, Target Identification, Platform Biology, Platform Chemistry, and Drug Discovery to ensure the ML Platform meets diverse needs — from enabling cutting-edge model development to scaling model application across research teams and future pharma partners
• This hybrid role is based in Toronto, and candidates must be located in or able to relocate to Toronto or the Greater Toronto Area (GTA)
• You'll develop the overall ML Platform vision and strategy in partnership with the ML Platform team and key internal and external stakeholders
• Define and maintain the roadmap for core ML Platform problem areas
• Deeply understand workflows and pain points of ML Scientists and Bioinformaticians across the company; translate those insights into well‑scoped problems, clear user stories, and prioritized backlog items
• Work closely with ML Scientists and ML Engineers to co‑design solutions — owning discovery, scoping requirements, and validating prototypes
• Partner with Tech Leads to plan and coordinate engineering work across multiple concurrent projects – including co-facilitating agile ceremonies to ensure smooth execution
• Support release planning, change management, and rollout activities to drive adoption of new ML Platform features
• Establish strong relationships with key stakeholders (ML Science, Target Identification, Engineering, Platform Biology, Platform Chemistry, Drug Discovery and Leadership); communicate team focus, progress, trade‑offs, and risks through regular updates, demos, and written documentation
• Collect feedback on platform usability, performance, and reliability; iterate on features and processes to enhance efficiency, quality, and reproducibility of ML workflows