Director, Machine Learning
(Calculated after EPF (12%), Gratuity (4.81%), Professional Tax & New Tax Regime FY 2025-26 deductions)
- Target Role:Director, Machine Learning
- Employer:Narvar
- Work Mode:REMOTE
- Location:Remote (Canada)
- Compensation:CAD 240,000 - 260,000 / annual
- Experience:2+ Years
- Recruiter SLA:7-Day Truth Teller Guarantee
- Candidate Fee:₹0 (Zero Placement Fee Guarantee)
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About the Role
Narvar is Growing! We're looking for a leader to own the machine learning systems behind post-purchase commerce — identity, fraud, and the intelligence layer that agentic AI runs on. Hundreds of millions of consumers interact with Narvar every year, across 20B+ orders and 1,300+ retail brands. That data powers three systems: Graphite , our identity resolution engine that ties fragmented consumer data into a single verified identity across retailers; IRIS , our fraud and returns-abuse detection engine built on top of it. You'll own the ML organization behind all of it — the models, the platform they run on, and the people who build them. This is a role for someone who wants a system with real consequences: fraud decisions that move retailer margin, identity resolution that agents make automated decisions on, and an adversary on the other side who adapts every quarter. For this role, you should be located in Canada and able to work within EST/EDT OR PST hours. We are fully remote. 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 agen
Requirements & Qualifications
- Strong problem solving foundations, knowledge of modern engineering practices, and interest in technology systems.
Perks & Benefits
- Official mentor support
- verified hiring timeline
- and career progression.
The employer seeks candidates proficient in the following core technologies:
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Verified Direct Application Link
Official position sourced directly from Narvar's verified career portal.
Apply directly on Narvar's portal using the link above. Use "Track with Truth Teller" to log your submission timestamp and automated 7-day follow-up countdown in your dashboard.
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How do I apply for Director, Machine Learning at Narvar?▼
Click "Apply on Official Site" on this page to be routed directly to Narvar's official applicant tracking system (EXTERNAL). RoleNest connects you directly with ₹0 candidate fees and activates automated 7-day follow-up telemetry in your dashboard.
What is the compensation or stipend for this role?▼
The verified package for this role is CAD 240,000 - 260,000 / annual (REMOTE). RoleNest enforces transparent compensation benchmarking to eliminate guessing before candidates invest time in assessments.
Who is eligible to apply for this opening?▼
Engineers with 2+ years of experience proficient in Data Science, Python, Predictive Modeling, Machine Learning are encouraged to apply.
How does RoleNest Truth Teller protect applicants from ghosting?▼
Truth Teller tracks candidate application timestamps and recruiter response telemetry. If Narvar does not update your application status within 7 business days, RoleNest provides polite, ATS-tailored follow-up email templates and highlights active matching roles to keep your momentum high.
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