[Hiring] Director, Machine Learning @Narvar

Role Description

We're looking for a leader to own the machine learning systems behind post-purchase commerce — identity, fraud, and the intelligence layer that agentic AI runs on.

You'll own the ML organization behind all of it — the models, the platform they run on, and the people who build them. This is a role for someone who wants a system with real consequences: fraud decisions that move retailer margin, identity resolution that agents make automated decisions on, and an adversary on the other side who adapts every quarter.

For this role, you should be located in Canada and able to work within EST/EDT OR PST hours. We are fully remote.

Day-to-day

• Own the ML charter across identity resolution, fraud and abuse detection, risk scoring, and consumer intelligence — strategy, roadmap, and delivery.
• Build and grow a high-performing, globally distributed team of ML engineers; hire, coach, and develop senior ICs and managers.
• Set the technical bar for how models get built, evaluated, deployed, and monitored — and hold the org to it.
• Push identity resolution coverage, precision, and profile classification accuracy against a measured, frozen-holdout baseline.
• Own IRIS model performance end-to-end: detection rate, false-positive rate, label quality, and the feedback loops that keep both honest as fraud patterns shift.
• Build the ML platform layer — feature stores, training pipelines, model registry, online serving, drift and performance monitoring.
• Partner with the AI engineering team so identity and risk signals are first-class inputs to NAVI's agent decisions.
• Work directly with Product, Engineering, Security, and Customer Success to translate messy retailer problems into ML problems worth solving.
• Own build-vs-buy and data-partner decisions, including the economics.
• Communicate model performance, risk, and tradeoffs credibly to executives, retailers, and the board.

Qualifications

• 12+ years in engineering with 5+ years managing ML or data teams, including managing managers or senior ICs.
• Engineer at heart — able to read a training pipeline, review a feature spec, and identify evaluation issues.
• Experience shipping ML systems that make consequential automated decisions in production.
• Deep experience with at least one of the following: entity resolution, fraud and abuse detection, risk scoring, or anomaly detection at scale.
• Understanding of what makes ML different from software: noisy labels, silent system failures, and misleading offline metrics.
• Opinions about evaluation — precision/recall tradeoffs on heavily imbalanced data, holdout hygiene, and feedback loops.
• Experience building or scaling ML infrastructure: feature pipelines, training orchestration, online model serving, monitoring, and alerting.
• Fluency in Python and comfort with a modern data stack (Spark, Airflow or equivalent, streaming, cloud data warehouses).
• Experience hiring and retaining engineers whose careers improved under your leadership.
• Ability to excel in a fast-paced environment with a flat structure.
• BS/MS in computer science, statistics, or equivalent background.

Bonus Points

• Experience with identity resolution or graph systems against third-party consumer data.
• Experience operating adversarial ML systems where attackers adapt to detection.
• Experience building real-time inference paths for identity or risk resolution.
• Experience in retail, payments, fintech, insurance, or marketplace trust & safety.
• Experience partnering closely with LLM/agent teams.
• Experience navigating privacy, consent, and data-governance constraints on consumer identity data.
• Experience standing up a function, platform, or data partnership from scratch.
• Use of modern tooling to increase leverage in development workflows.

Why ML at Narvar?

• Genuinely rare data and genuinely hard problems.
• Cross-retailer visibility across 20B+ orders to see coordinated fraud patterns.
• Adversarial and unsolved problems in returns fraud and policy abuse.
• Measurable stakes: fraud loss, false-positive cost, identity coverage, resolution rate.
• Opportunity to own a trust layer for returns, claims, and delivery promises.
• Greenfield work with decisions still being made.

Why Narvar (from an engineer's perspective)?

• 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.

Salary Range

The estimated annual salary for this position is $240,000 - $270,000 CAD. This does not include other components such as annual bonus, equity, and benefits.

Equal Opportunity Employer

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.