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 ForWe 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 pointsThese 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 01 - 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
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.
Narvar Salary Range
$240,000-$260,000 CAD
