Senior AI Product Manager

Role Overview

• Translate experimental and model‑level results into clear business impact (e.g., fraud loss reduction, automation lift, approval‑rate improvements, operational efficiency).
• Define what “success” means for each AI technique or model before it reaches production.
• Frame why the work matters to internal stakeholders including Risk, Customer Success, Sales, Finance, and Executive Leadership.
• Partner with Data Science to move models from offline experimentation to online testing and controlled rollout.
• Help define evaluation frameworks, guardrails, and decision thresholds required for production use.
• Ensure trade‑offs (accuracy, latency, cost, operational complexity) are explicit and intentional.
• Act as the primary product interface between SAIL, Risk, and Engineering.
• Ensure there is a predictable process for ramping traffic, managing risk approvals, and resolving blockers.
• Secure engineering capacity and infrastructure alignment by grounding asks in committed outcomes and timelines.
• Identify which customer segments or merchants are realistic early adopters for new decisioning approaches.
• Define the value story: what problem is solved, how success is measured, and what changes operationally for the customer.
• Partner with Customer Success to estimate upside, prerequisites, and deployment complexity.
• Maintain a clear view of milestones, dependencies, risks, and decisions across active SAIL initiatives.
• Keep stakeholders informed with the right level of detail to sustain momentum without over‑communicating.
• Ensure experimental work does not stall due to ambiguity, misalignment, or lack of ownership.

Requirements

• 6+ years of Product Management experience, with significant exposure to AI‑ or ML‑driven products.
• Proven experience working closely with Data Science and Engineering on model‑centric systems.
• Strong ability to translate technical performance into business and customer outcomes.
• Comfort operating in highly ambiguous, fast‑moving environments.
• Excellent written and verbal communication skills across technical and non‑technical audiences.
• Strong sense of ownership, judgment, and bias toward 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 working in fraud, risk, trust & safety, or operationally‑constrained environments.
• Background in scaling early or experimental products into production systems.

Benefits

• Discretionary Time Off Policy (Unlimited!)
• 401K Match
• Stock Options
• Annual Performance Bonus or Commissions
• Paid Parental Leave (12 weeks)
• On-Demand Therapy for all employees & their dependents
• Dedicated learning budget through Learnerbly
• Health Insurance
• Dental Insurance
• Vision Insurance
• Flexible Spending Account (FSA)
• Short Term and Long Term Disability Insurance
• Life Insurance
• Company Social Events
• Signifyd Swag