Senior Product Manager

At Signifyd, we empower businesses to thrive in digital commerce by eliminating fraud and enabling seamless customer experiences. Our mission is to build trust at every step of the customer journey — from checkout through post‑purchase.

Signifyd AI Lab (SAIL) is our applied AI and machine‑learning innovation group. SAIL partners closely with Risk, Engineering, and Data Science to develop and productionize next‑generation decisioning techniques that materially improve fraud outcomes, automation, and customer experience across Signifyd’s platform.

The Role: 

We are looking for a Senior AI Product Manager to work with SAIL, owning the path from experimentation to real‑world impact for advanced AI and ML initiatives.

This role sits at the intersection of applied research, productization, and business impact. You will help turn cutting‑edge models and techniques into outcomes that Risk teams can trust, Engineering teams can scale, and customers can ultimately benefit from.

The ideal candidate is deeply comfortable with ambiguity, understands how to translate model performance into business value, and can drive alignment across Data Science, Engineering, Risk, and GTM without slowing momentum.

This is not a feature PM role. It is a systems‑ and outcomes‑oriented role, focused on decisioning quality, scalability, cost, latency, and downstream impact.

Responsibilities:

• 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.

While exact projects will evolve, this role will typically involve:

• Translating advanced AI and ML experiments into clear product narratives and deployment plans.
• Defining how new decisioning approaches integrate into existing systems, workflows, and risk controls.
• Helping standardize evaluation, comparison, and rollout criteria across different modeling approaches.
• Aligning model innovation with infrastructure realities such as cost budgets, latency constraints, and scalability requirements.
• Ensuring internal teams understand what SAIL is building, when it will matter, and how it will show up for customers.

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.

#LI-Remote

Benefits in our US offices:

• 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

Compensation: 

In the United States, each work location is assigned a specific pay zone, which determines the salary range for a given position. The starting base salary for the selected candidate will be based on a variety of factors, including job-related skills, experience, qualifications, geographic location, and current market conditions.

Base Salary Ranges by Pay Zone:

• Tier 1 (NYC/SF Bay Area/Seattle): $165,000 – $205,000 annually
• Tier 2 (DC Metro/Austin/Chicago/Denver/Boston/Los Angeles/San Diego): $155,000 – $195,000 annually
• Tier 3 (US - All Other): $145,000 – $185,000 annually

Equity: This role is eligible for a stock option grant of 6,000-8,000 stock options, based on the position level and internal compensation guidelines. 

Bonus: This role is eligible for an annual performance bonus of up to 10% of base salary.

We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.