Director, Machine Learning
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Job Description
Director of Machine Learning leading Narvar’s identity, fraud, and risk systems for post-purchase commerce. Managing ML teams and infrastructure powering retailer and agentic-AI decisions.
Responsibilities:
- Own the ML charter across identity resolution, fraud and abuse detection, risk scoring, and consumer intelligence, including strategy, roadmap, and delivery
- Build and grow a high-performing, globally distributed team of ML engineers
- Hire, coach, and develop senior individual contributors and managers
- Set standards for how models are built, evaluated, deployed, and monitored
- Improve identity resolution coverage, precision, and profile classification accuracy against measured holdout baselines
- Own IRIS model performance, including detection rate, false-positive rate, label quality, and feedback loops
- Build the ML platform layer, including feature stores, training pipelines, model registry, online serving, and drift and performance monitoring
- Partner with AI engineering so identity and risk signals inform NAVI agent decisions
- Collaborate with Product, Engineering, Security, and Customer Success to translate retailer problems into ML initiatives
- Own build-versus-buy and data-partner decisions, including economics
- Communicate model performance, risk, and tradeoffs to executives, retailers, and the board
Requirements:
- Located in Canada and able to work within EST/EDT or PST hours
- 12+ years in engineering
- 5+ years managing ML or data teams, including managing managers or senior ICs
- Ability to read a training pipeline, review a feature specification, and evaluate whether an evaluation measures the right thing
- Experience shipping ML systems that make consequential automated decisions in production and owning them after launch, including drift, retraining, and incidents
- Deep experience with at least one of: entity resolution / identity graphs, fraud and abuse detection, risk scoring, or anomaly detection at scale
- Understanding of noisy and delayed labels, silent system failures, misleading offline metrics, and adaptive adversaries
- Experience with precision/recall tradeoffs on heavily imbalanced data, holdout hygiene, and feedback loops affecting future labels
- Experience building or scaling ML infrastructure, including feature pipelines, training orchestration, online model serving, monitoring, and alerting
- Fluency in Python
- Comfort with Spark, Airflow or equivalent, streaming, cloud data warehouses, and GCP
- Experience hiring and retaining engineers and developing their careers
- Ability to excel in a fast-paced, flat-structure environment
- BS/MS in computer science, statistics, or an equivalent background
Benefits:
- Annual bonus
- Equity
- Benefits
- Fully remote work
- Low ego, high trust, and room to do your best work
- Professional wins, baby showers, birthday parties, and other celebrations
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