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