Staff Data Product Manager (Machine Learning)

Jobgether; Canada; Remote; CAD 164.000 - 248.000; Vollzeit

Benefits dieser Stelle

Remote-first Canada

Monthly healthcare stipend

RRSP with 4% employer match

Equipment stipend

Wellness stipend

Wi‑Fi and cell phone stipends

Vacation and sabbatical options

Hackathons and cross-department events

Zusammenfassung

Jobgether in Canada seeks a Staff Data Product Manager (Machine Learning) to define and lead the ML product strategy across multiple business verticals. You will work with Data Scientists, ML Engineers, and Product teams to take models from concept through production and adoption, shaping the ML roadmap and business impact in a remote-first environment.

The role emphasizes ownership, measurable outcomes, and collaboration with stakeholders to deliver scalable ML products that improve revenue,

Qualifikationen

• 7+ years of experience in Data Product Management, Data Science, Machine Learning, or related technical discipline.
• Deep understanding of applied ML concepts including training data, ground-truth definitions, loss functions, precision and recall.
• Experience with ML platforms, feature pipelines, batch and real-time inference, latency, deployment workflows.
• Experience working with Data Scientists and ML Engineers on technical decisions and dependencies.
• Familiarity with Databricks, Snowflake, AWS or equivalent solutions.
• Strong PM capabilities: roadmap ownership, prioritization, requirements, end-to-end delivery.
• Ability to translate business needs into ML product objectives with measurable outcomes.
• Excellent analytical and problem-solving skills with data-informed decisions.
• Outstanding communication and stakeholder management; able to influence leaders.
• Cross-functional leadership to align multiple teams on shared objectives.
• Strategic thinking balanced with immediate priorities.
• Bachelor's or advanced degree in relevant discipline or equivalent experience.

Aufgaben

• Own and lead ML product roadmap across multiple business verticals.
• Collaborate with Data Scientists and ML Engineers on modeling approaches and feature strategies.
• Guide migrations from concept to production deployment and adoption.
• Define ML product objectives, technical requirements, and success criteria.
• Drive ML initiatives from discovery through production and business adoption.
• Coordinate cross-functional execution to integrate model predictions into experiences and workflows.
• Ensure production readiness with ownership, dependencies, and performance alignment.
• Design experiments, A/B tests, and measurement frameworks to show value.
• Track and communicate metrics: performance, reliability, adoption, business impact.

Kenntnisse

Data product management

Machine learning product strategy

Stakeholder management

Cross-functional leadership

Roadmap ownership

A/B testing

Analytical thinking

Communication skills

Ausbildung

Bachelor's or advanced degree in CS/Engineering/Stats

Tools

Databricks

Snowflake

AWS

Jobbeschreibung

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Product Manager (Machine Learning) based in Canada.

We are seeking an experienced Staff Data Product Manager to define and lead the machine learning product strategy across a growing, data-driven organization. In this strategic role, you will identify high-impact opportunities and transform complex business challenges into scalable ML products that deliver measurable results. You will work closely with Data Scientists, ML Engineers, Product teams, and business stakeholders to take predictive models from initial concept through production deployment and adoption. Your expertise will help shape initiatives involving customer lifetime value prediction, lead scoring, personalization, and intelligent product recommendations. You will influence technical decisions, establish product priorities, and ensure that machine learning investments translate into meaningful business outcomes. This is an opportunity to provide strategic leadership, shape the long-term ML roadmap, and drive innovation within a collaborative, remote-first environment.

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.