Staff Data Product Manager (Machine Learning)

Accountabilities

Own and lead the machine learning product roadmap across multiple business verticals, identifying and prioritizing initiatives based on business impact, technical feasibility, and available engineering capacity.
Partner closely with Data Scientists and ML Engineers to evaluate modeling approaches, challenge assumptions, and guide decisions related to training data quality, ground-truth definitions, precision and recall, feature strategies, and model evaluation.
Navigate complex machine learning infrastructure decisions, including model serving, feature pipelines, batch versus real-time inference, latency requirements, deployment workflows, and production dependencies.
Translate ambiguous business challenges into clearly defined ML product objectives, technical requirements, delivery plans, and measurable success criteria.
Drive machine learning initiatives from initial discovery and prioritization through development, production deployment, and business adoption.
Coordinate cross-functional execution across Data Science, Data Engineering, Product Engineering, and business teams to integrate model predictions into consumer experiences, real-time decision-making systems, and operational workflows.
Ensure every ML product launch meets production-readiness standards, with clearly defined ownership, validated dependencies, and performance aligned with technical and business requirements.
Collaborate with Data Science and Analytics teams to design experiments, A/B tests, and causal measurement frameworks that demonstrate the incremental value and effectiveness of machine learning solutions.
Define, track, and communicate success metrics covering predictive performance, model reliability, product adoption, and business outcomes.
Evaluate and communicate model performance, technical trade-offs, limitations, and commercial impact to senior stakeholders, ensuring transparency and alignment on strategic decisions.
Establish the long-term vision and strategy for machine learning products, influencing technical direction and product investment decisions across the organization.
Provide leadership beyond direct product ownership by mentoring other Product Managers, supporting cross-functional partners, and promoting best practices in data and machine learning product management.
Balance competing priorities and make informed decisions about which initiatives to pursue, defer, or discontinue to maximize the overall value of the ML portfolio.
Requirements
At least 7 years of experience in Data Product Management, Data Science, Machine Learning, or a related technical discipline, with a proven track record of delivering production-grade machine learning or data products.
Deep understanding of applied machine learning concepts, including training data, ground-truth definitions, loss functions, precision and recall, overfitting, and model evaluation methodologies.
Strong knowledge of machine learning platforms and infrastructure, including model serving, feature pipelines, batch and real-time inference systems, latency considerations, and deployment workflows.
Demonstrated experience working directly with Data Scientists and ML Engineers, challenging technical assumptions, evaluating modeling trade-offs, and managing complex technical dependencies.
Familiarity with modern data and machine learning technologies and platforms such as Databricks, Snowflake, AWS, or equivalent solutions.
Strong product management capabilities, including roadmap ownership, prioritization, product strategy, requirements definition, and end-to-end delivery of complex technical initiatives.
Proven ability to translate business needs into clear machine learning product objectives and connect technical investments to measurable outcomes such as revenue growth, customer lifetime value, conversion rates, and marketing efficiency.
Excellent analytical and problem-solving skills, with the ability to assess technical feasibility, evaluate product performance, and make data-informed decisions.
Outstanding communication and stakeholder management abilities, including experience influencing senior technical and business leaders without relying on formal authority.
Strong cross-functional leadership skills, with the ability to align multiple teams around shared objectives and drive initiatives forward in complex organizational environments.
Strategic thinking and sound business judgment, with the ability to balance long-term product vision against immediate business priorities and technical constraints.
Bachelor's or advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related discipline, or equivalent practical experience.
Ability to operate independently, demonstrate ownership, and provide technical and strategic leadership in a collaborative, fast-paced environment.
Benefits
Competitive annual base salary ranging from CA$164,000 to CA$248,000, depending on education, relevant knowledge, skills, experience, and location.
Fully remote, remote-first working environment, with the flexibility to work from anywhere in Canada, excluding Quebec.
Monthly healthcare stipend to support personal and family well-being.
Vacation time through a dedicated rejuvenation policy, in addition to official public holidays in your province.
Paid sabbatical opportunities to recharge, pursue personal interests, and develop new skills.
Monthly wellness stipend to support physical and emotional well-being.
Monthly Wi-Fi and cell phone stipends.
Work-from-home equipment stipend to help create a comfortable and productive workspace.
Registered Retirement Savings Plan (RRSP) with a 4% employer match, available one month after hiring.
Access to financial wellness resources, personalized guidance, and unlimited consultations with a Certified Financial Planner (CFP) through Northstar.
Employee Resource Groups supporting parents, diversity and inclusion, women, LGBTQIA+ communities, and other employee communities.
Opportunities to participate in hackathons, collaborative team events, and cross-departmental initiatives.
Company-wide events focused on employee appreciation, engagement, and charitable initiatives.
Inclusive and supportive workplace culture that encourages professional development, collaboration, innovation, and work-life balance.