Director, Machine Learning

This is a fully remote position, open to applicants in Canada.

📋 Description

• Take ownership of the machine learning (ML) charter encompassing identity resolution, fraud and abuse detection, risk scoring, and consumer intelligence, which includes strategy, roadmap, and execution.

• Build and expand a high-performing, globally distributed team of ML engineers.

• Recruit, mentor, and develop senior individual contributors and managers.

• Establish standards for the construction, evaluation, deployment, and monitoring of models.

• Enhance identity resolution coverage, precision, and profile classification accuracy against established holdout baselines.

• Take charge of IRIS model performance, encompassing detection rates, false-positive rates, label quality, and feedback loops.

• Develop the ML platform layer, which includes feature stores, training pipelines, model registry, online serving, and monitoring for drift and performance.

• Collaborate with AI engineering to ensure that identity and risk signals guide NAVI agent decisions.

• Work alongside Product, Engineering, Security, and Customer Success to convert retailer challenges into ML initiatives.

• Make decisions regarding build-versus-buy and data partnerships, including economic considerations.

• Communicate model performance, risks, and trade-offs to executives, retailers, and the board.

⛳️ Requirements

• Must be located in Canada and available to work within EST/EDT or PST time zones.

• Over 12 years of experience in engineering.

• More than 5 years of experience managing ML or data teams, including oversight of managers or senior individual contributors.

• Capability to interpret a training pipeline, analyze a feature specification, and assess whether an evaluation accurately measures the intended criteria.

• Proven experience in deploying ML systems that make significant automated decisions in production, along with ongoing ownership responsibilities post-launch, including drift management, retraining, and incident response.

• Extensive experience in at least one of the following: entity resolution/identity graphs, fraud and abuse detection, risk scoring, or large-scale anomaly detection.

• Solid understanding of issues related to noisy and delayed labels, silent system failures, misleading offline metrics, and adaptive adversaries.

• Familiarity with precision/recall trade-offs in heavily imbalanced datasets, holdout hygiene, and feedback loops that impact future labels.

• Experience in building or scaling ML infrastructure, including feature pipelines, training orchestration, online model serving, monitoring, and alerting.

• Proficient in Python.

• Comfortable with Spark, Airflow or similar tools, streaming technologies, cloud data warehouses, and Google Cloud Platform (GCP).

• Experience in hiring and retaining engineers while fostering their professional growth.

• Ability to thrive in a fast-paced, flat-structured environment.

• Bachelor’s or Master’s degree in computer science, statistics, or a related field.

🏝️ Benefits

• Annual bonus.

• Equity options.

• Comprehensive benefits package.

• Fully remote work opportunity.

• Collaborative culture with low ego, high trust, and the freedom to excel in your work.

• Celebrations of professional achievements, baby showers, birthday parties, and other events.