Staff Data Product Manager (Machine Learning) - Canada

nerdwallet; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

NerdWallet, Inc. is seeking a Staff Data Product Manager to own the ML product roadmap and partner with Data Science, ML Engineering, and Product teams. You will shape model objectives, data quality, and serving architectures to drive business outcomes.

The role focuses on turning ML ideas into production-ready models, aligning technical decisions with business value and measurable success metrics. You will report to the VP, Data Analytics.

Qualifications

• 7+ years in Data Product Management, Data Science, ML, or related field with shipping production ML or data products.
• Deep fluency in applied ML: training data, ground truth, loss functions, precision/recall, overfitting, evaluation.
• Strong understanding of ML platforms and infrastructure: model serving, feature pipelines, latency, production dependencies.

Responsibilities

• Own and prioritize the ML roadmap across verticals, balancing impact, feasibility, and capacity.
• Collaborate with Data Science, ML Engineering, and Product teams to deploy model predictions into experiences and workflows.
• Ensure launches are production-ready with clear ownership and validated performance against requirements.
• Define measurable success criteria and monitor predictive performance and business impact.

Skills

Data product management

Machine Learning

Model evaluation

Tools

ML platforms

Model serving

Feature pipelines

Job description

NerdWallet’s mission is to provide clarity for all of life’s financial decisions. Machine learning is how we get there at scale. It helps us understand our consumers, personalize their experiences, and connect them with the financial products that fit their needs.We’re hiring a Staff Data Product Manager to own the ML product roadmap across NerdWallet. You’ll work with our business verticals, Data Scientists, and Engineering teams to find the highest-impact opportunities and take predictive models from idea to production to adoption. The work ranges from customer lifetime value prediction to lead scoring to personalized product recommendations.This is a product leadership role for someone who is technically fluent in ML. You won’t build models or write production code, but you’ll be a credible, peer-level partner to the people who do. You’ll challenge assumptions on ground truth, evaluation, and serving architecture, and you’ll tie every model to a business outcome.This role reports to the VP, Data Analytics.

Where you can make an impact:

• Partner deeply with Data Science and ML EngineeringDig into modeling trade-offs, ground-truth definitions, training data quality, precision/recall, and feature strategy alongside Data Scientists and ML Engineers.
• Navigate ML infrastructure decisions, including model serving, feature pipelines, batch vs. real-time inference, latency, and production dependencies.
• Turn ambiguous business problems into clear model objectives, technical requirements, and measurable success criteria.
• Own the roadmap and drive models into productionOwn and prioritize the ML roadmap across verticals, balancing business impact, technical feasibility, and engineering capacity. You’ll decide what to build, defer, or say no to.
• Work across Data Science, Data Engineering, Product Engineering, and business teams to get model predictions into consumer experiences, real-time decisioning systems, and business workflows.
• Make sure every launch is production-ready, with clear ownership of dependencies and validated performance against technical and business requirements.
• Measure what mattersWork with Data Science and Analytics on experiments, A/B tests, and causal measurement that prove the incremental impact of ML.
• Define and monitor success criteria covering predictive performance, reliability, adoption, and business outcomes.
• Explain model performance, trade-offs, limitations, and revenue impact clearly to senior stakeholders.
• Lead beyond your own roadmapSet the long-term vision for ML products, influence without formal authority, and mentor other product managers and partners.

Your experience:

• 7+ years in Data Product Management, Data Science, Machine Learning, or a related technical field, with a track record of shipping production ML or data products
• Deep fluency in applied ML: training data, ground truth, loss functions, precision/recall, overfitting, and model evaluation
• Strong understanding of ML platforms and infrastructure: model serving, featu