AI Technical Lead

BRP•Montreal, QC

5d•Hybrid

About The Position

Be a part of a fast-paced culture and community in a startup-like environment inside one of the most successful organizations in Québec. We are focused on building a highly talented and ambitious team from the ground up that revolutionizes the way we work around Data & Analytics at BRP. We are a team that has the potential to unlock tremendous value for the organization and you have the unique opportunity to be part of it from the beginning. Join BRP's central Data & Analytics team (DNA) as an AI Advisor and bring your analytical skills and business insight to our dynamic team. This role is perfect for someone who seeks

Requirements

MSc or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related field
5+ years of experience in a senior Data Science, AI Research, or Applied AI role or equivalent depth through top-tier consulting with deep AI specialization
Strong theoretical grounding in ML/AI: understands what is under the hood of LLMs, classical ML, deep learning, and optimization
Naturally curious and skeptical, able to ask the questions others don't know to ask
Excellent communication skills; able to adapt register from technical deep-dive to executive summary
Proficiency in French and English, the team operates primarily in English

Nice To Haves

Experience evaluating or auditing AI-powered solutions from external suppliers
Exposure to AI risk topics: fairness, explainability, data governance, regulatory compliance
Ability to write code or run experiments when a decision demands it, without being primarily a builder
Knowledge of the AI/ML vendor landscape across relevant domains
Background that combines technical depth with business or strategic exposure

Responsibilities

Serve as the primary AI/ML technical reference when evaluating supplier solutions or internal AI initiatives
Challenge supplier AI claims: interrogate model architecture, training data, performance metrics, limitations and risks at a level most business stakeholders cannot
Define evaluation frameworks and success criteria for any AI solution under consideration
Occasionally deep-dive into code, benchmarks or proofs-of-concept when a decision requires it
Work alongside the AI Product Owner to inform build vs. buy decisions with technical depth
Identify hidden risks before they become problems: bias, model drift, explainability gaps, vendor lock-in, data sovereignty
Ensure every AI initiative, built internally or sourced externally, is grounded in technical reality, not marketing claims
Bridge complex AI/ML concepts and business realities across all levels of the organization
Make AI understandable for business stakeholders, and business complexity actionable for technical teams
Operate with equal credibility in executive conversations and technical deep-dives
Stay current on the AI landscape, emerging techniques, and vendor developments
Anticipate relevant opportunities before suppliers bring them to the door
Contribute to the maturity and AI culture of the broader AIML organization