Director, Machine Learning

Role overview Lead the machine learning organization responsible for identity resolution, fraud and abuse detection, risk scoring, and consumer intelligence in post-purchase commerce. The director owns strategy, people, models, and the platform that runs them, with a focus on measurable production outcomes as fraud patterns and automated commerce workflows evolve.

Responsibilities - Set the ML charter, roadmap, and delivery priorities across identity and risk capabilities. - Hire, coach, and develop a distributed team of ML engineers, including senior individual contributors and managers. - Establish standards for model development, evaluation, deployment, and monitoring. - Track identity resolution quality and fraud model performance using measured baselines, including precision, detection rates, false positives, label quality, and feedback loops. - Build platform capabilities such as feature pipelines, training orchestration, model registry, online serving, and drift monitoring. - Partner with AI engineering, product, security, engineering, and customer-facing teams to prioritize useful problems and communicate model performance, risks, and tradeoffs. - Guide build-versus-buy and external data partnership decisions, including their economics.

Requirements - 12+ years in engineering and 5+ years managing ML or data teams, including senior contributors or other managers. - Experience shipping and operating consequential ML decisions in production, including handling drift, retraining, incidents, and ongoing evaluation. - Deep expertise in at least one area such as identity resolution, identity graphs, fraud and abuse detection, risk scoring, or large-scale anomaly detection. - Strong understanding of evaluation for imbalanced data, holdout integrity, noisy or delayed labels, and feedback loops that can distort future training data. - Experience building ML infrastructure for feature pipelines, training, low-latency serving, monitoring, and alerting. - Fluency in Python and familiarity with data tools such as Spark, Airflow or equivalents, streaming, and cloud data warehouses; ability to guide technical work directly. - Demonstrated success hiring and developing engineers, and influencing through trust in a fast-moving, low-hierarchy environment.

Nice to have - A degree in computer science, statistics, or equivalent experience. Experience with identity or graph systems and third-party consumer data is a plus.

Benefits and work setup - Fully remote, with candidates based in Canada and able to work Eastern or Pacific time. The stated salary range is CAD $240,000–$260,000, with bonus, equity, and benefits described separately.