Director, Machine Learning

These aren't hard requirements, but strong indicators:

You've worked on identity resolution or graph systems against third-party consumer data (credit bureau, telecom, or similar), including the matching logic and confidence scoring underneath

You've operated adversarial ML systems where attackers actively adapt to your detection

You've built real-time inference paths where identity or risk has to be resolved inside a request budget

You've worked in retail, payments, fintech, insurance, or marketplace trust & safety

You've partnered closely with LLM/agent teams and understand how structured ML signals ground agentic decisions

You've navigated privacy, consent, and data-governance constraints on consumer identity data

You've done 0→1 — stood up a function, a platform, or a data partnership from nothing

You use modern tooling (including AI-assisted development workflows) to increase leverage, not outsource thinking

Because the data is genuinely rare and the problems are genuinely hard.

The dataset is a moat. Cross-retailer visibility across 20B+ orders means we can see coordinated fraud patterns no single retailer can — a returns abuser hitting five brands looks like a good customer at each one.

The problem is adversarial and unsolved. Returns fraud and policy abuse are growing faster than retail itself, and the counterparty adapts. There is no off-the-shelf answer.

The stakes are measurable. Fraud loss, false-positive cost, identity coverage, resolution rate. Not vanity metrics.

It feeds something bigger. As AI shopping agents enter commerce, every agent will need a trust layer for returns, claims, and delivery promises. Identity and risk are that layer — and you'd own it.

The work is greenfield enough to matter. Real-time resolution, cold-start modeling, historical backfill, new data partnerships — these are decisions still being made, not legacy you inherit.

Real scale, real customers, real consequences

Startup-level ownership with platform-level impact

Teams that value thinking, judgment, and responsibility

Low ego, high trust, and room to do your best work #LI-Remote

We're on a mission to simplify the everyday lives of consumers. Post-purchase is a critical phase of the customer journey. That's why we created Narvar - a platform focused on driving customer loyalty through seamless post-purchase experiences that allow retailers to retain, engage, and delight customers. If you've ever bought something online, there's a good chance you've used our platform!

From the hottest new direct-to-consumer companies to retail’s most renowned brands, Narvar works with GameStop, Neiman Marcus, Sonos, Nike, and 1300+ other brands. With hubs in San Francisco, Atlanta, London, and Bangalore, we've served over 125 million consumers worldwide across 10+ billion interactions, 38 countries, and 55 languages.

Pioneering the post-purchase movement means navigating into the unknown. Our team thrives on this sense of adventure while nurturing a mindset of innovation. We're a home for big hearts and we leave our egos at the door. We work hard but we always make time to celebrate professional wins, baby showers, birthday parties, and everything in between.

We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Below is the estimated annual salary for this position and does not include the other components that make up a Narvar offer including: annual bonus, equity, and benefits. The range reflects the minimum and maximum target for new hire salaries for the position across Canada. Within the range, individual compensation packages are based on factors unique to each candidate, including but not limited to, skill set, education and certifications, and work location.

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