Responsibilities

• Translate experimental and model-level results into clear business impact such as fraud loss reduction, automation lift, and approval-rate improvements.
• Define success criteria for each AI technique or model before production.
• Communicate the importance of work to internal stakeholders including Risk, Customer Success, Sales, Finance, and Executive Leadership.
• Partner with Data Science to progress models from offline experiments to online testing and rollout.
• Define evaluation frameworks, guardrails, and decision thresholds for production use.
• Ensure trade-offs like accuracy, latency, cost, and operational complexity are explicit and intentional.
• Act as the primary product interface between SAIL, Risk, and Engineering.
• Manage processes for traffic ramp-up, risk approval, and issue resolution.
• Secure engineering capacity and infrastructure alignment based on outcomes and timelines.
• Identify realistic early adopter customers for new AI decisioning approaches.
• Define value proposition including problem solved, success measurement, and operational changes.
• Collaborate with Customer Success for estimating upside, prerequisites, and deployment complexity.
• Maintain clear view of milestones, dependencies, risks, and decisions across SAIL initiatives.
• Communicate effectively with stakeholders to sustain momentum.
• Prevent experimental work from stalling due to ambiguity or misalignment.

What You’ll Work On (Examples)

• Translating advanced AI and ML experiments into product narratives and deployment plans.
• Defining integration of new decisioning approaches into existing systems, workflows, and controls.
• Standardizing evaluation, comparison, and rollout criteria for models.
• Aligning model innovation with infrastructure constraints like cost, latency, and scalability.
• Ensuring internal teams understand developments and their impact on customers.

Requirements

• 6+ years of Product Management experience with significant AI or ML product exposure.
• Experience collaborating closely with Data Science and Engineering on model-centric systems.
• Ability to translate technical performance into business and customer outcomes.
• Comfortable operating in ambiguous and fast-moving environments.
• Excellent written and verbal communication skills to technical and non-technical audiences.
• Strong ownership, judgment, and bias towards action.

Nice to have

• Experience with decisioning systems, risk models, or large-scale ML platforms.
• Familiarity with model evaluation, experimentation frameworks, and feedback loops.
• Experience in fraud, risk, trust & safety, or operationally-constrained environments.
• Background scaling early or experimental products into production.