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Quandri · 1 month ago

Staff AI/ML Product Manager

Vancouver, British Columbia, Canada

Full-time

Hybrid

Senior Level, Lead/Staff

CAD$170K/yr - CAD$200K/yr

5+ years exp

Quandri is a company dedicated to transforming the insurance industry through AI-driven automation and data insights. The Staff AI/ML Product Manager will own the product lifecycle for machine learning features, manage AI capabilities, and drive execution excellence across multiple initiatives.

Artificial Intelligence (AI)Industrial AutomationInsuranceInsurTechMachine Learning

Responsibilities

Execute Intelligence platform roadmap and contribute meaningfully to 2026 planning based on customer insights and technical learnings. Own requirements gathering with deep problem understanding, uncovering root needs beyond surface-level requests. Design product experiences that build user trust in AI through transparency, explainability, and graceful failure handling

Serve as the primary interface between the Intelligence engineering team and Product, Implementation, Sales, Marketing, and Leadership teams. Filter and prioritize incoming requests from 3 product teams to protect engineering focus while addressing critical needs. Facilitate feedback loops to collect ground truth data, stakeholder validation, and user insights

Partner with the Director of Engineering on project planning and delivery tracking across 3-4 parallel initiatives. Drive iterative development cycles with rapid feedback incorporation, shipping incremental value rather than waiting for perfection. Coordinate data labeling and ground truth collection efforts to improve model accuracy and validate product improvements

Engage with engineers on architecture, data pipelines, and model performance without needing to code. Translate model performance metrics (accuracy, precision, recall) into business impact. Understand ML product lifecycle including data requirements, model training and validation, feedback loops, and continuous learning systems

Build feedback loops to continuously improve intelligence capabilities based on ground truth data and user insights. Identify opportunities to leverage intelligence across multiple products and create incremental value through shared learning. Support strategic product decisions with technical insights from hands-on execution

Qualification

Product managementML/AI product lifecycleTechnical aptitudeSQLPythonStakeholder managementBias toward actionCommunication skills

Required

5+ years of product management experience with proven track record of shipping products with measurable business impact in fast-paced environments

2+ years shipping ML/AI products with demonstrated understanding of the ML product lifecycle from data collection through model deployment and iteration

Platform product experience with background working on products or features that serve multiple internal or external customers and managing competing stakeholder needs

Strong technical aptitude with comfort working in SQL, ability to read Python code, and solid understanding of ML fundamentals including model evaluation and data quality requirements

Complex stakeholder management with proven ability to navigate conflicting priorities across multiple teams and organizational levels

Excellent communication skills with ability to explain technical concepts to business audiences and translate business needs into actionable technical requirements

Bias toward action with comfort operating in ambiguous environments and making decisions with imperfect information

Preferred

Experience with data labeling, ground truth collection, or model evaluation workflows in production ML systems

Previous work at companies with strong AI product culture such as enterprise ML vendors, AI-native SaaS companies, or companies known for ML excellence

Familiarity with B2B SaaS metrics, enterprise sales cycles, and customer success dynamics in technical products

Vancouver-based or willing to relocate with enthusiasm for hybrid work environment and in-person collaboration

Experience with LLMs, RAG architectures, or modern ML infrastructure tools such as vector databases or model monitoring platforms

Background in insurance technology, regulated industries, or complex B2B workflows