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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in managing machine learning initiatives, including model performance evaluation, infrastructure scaling, and team development. Proficient in building and deploying ML systems that drive automated decision-making while ensuring precision and accuracy in identity resolution and fraud detection.
Highest-signal resume keywords
Machine Learning ManagementModel Performance EvaluationML Infrastructure ScalingTeam DevelopmentIdentity Resolution Expertise
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningModel EvaluationEntity ResolutionFraud DetectionRisk ScoringAnomaly DetectionPrecision/Recall TradeoffsFeature PipelinesTraining OrchestrationAutomated Decision-Making
Soft Skills
Team LeadershipCoachingCommunicationCollaborationAdaptability
Tools & Technologies
PythonSparkAirflowCloud Data WarehousesGCP
Certifications & Qualifications
BS/MS in Computer ScienceStatistics
Industry Keywords
Identity ResolutionFraud DetectionRisk ScoringML SystemsData Management
Tech Stack
Tools & technologiesAirflowCloudGoogle Cloud PlatformPythonSpark
About the role
Key responsibilities & impact- 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
What you’ll need- 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
Comp & perks- 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