Narvar is Growing! 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.

Hundreds of millions of consumers interact with Narvar every year, across 20B+ orders and 1,300+ retail brands. That data powers three systems: Graphite, our identity resolution engine that ties fragmented consumer data into a single verified identity across retailers; IRIS, our fraud and returns-abuse detection engine built on top of it.

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 — not against vibes
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 — so model velocity isn't bottlenecked on infrastructure
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 — and to say no to the ones that aren't
Own build-vs-buy and data-partner decisions (third-party identity data, enrichment providers), including the economics
Communicate model performance, risk, and tradeoffs credibly to executives, retailers, and the board

What We're Looking For

We care more about judgment and ownership than credentials.

You're likely a strong fit if you:

Have 12+ years in engineering with 5+ years managing ML or data teams, including managing managers or senior ICs
Are an engineer at heart — you can still read a training pipeline, review a feature spec, and tell when an eval is measuring the wrong thing
Have shipped ML systems that make consequential automated decisions in production, and have owned them after launch — drift, retraining, incidents, and all
Have deep experience with at least one of: entity resolution / identity graphs, fraud and abuse detection, risk scoring, or anomaly detection at scale
Understand what makes ML different from software: labels are noisy and delayed, systems fail silently, offline metrics lie, and last quarter's model is fighting last quarter's adversary
Have opinions about evaluation — precision/recall tradeoffs on heavily imbalanced data, holdout hygiene, feedback loops where the model's own decisions contaminate future labels
Have built or scaled ML infrastructure: feature pipelines, training orchestration, online model serving with real latency budgets, monitoring and alerting
Are fluent in Python and comfortable in a modern data stack (Spark, Airflow or equivalent, streaming, cloud data warehouses — we run on GCP)
Have hired well, retained well, and can point to engineers whose careers got better on your watch
Excel in a fast-paced environment with a flat structure — you set direction by earning trust, not by mandate
Have a BS/MS in computer science, statistics, or an equivalent background

Bonus points

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 i

Director, Machine Learning at Narvar