Staff Data Product Manager (Machine Learning)
New
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NerdWalletFinancial technology
This is a remote position and a person can be located anywhere in Canada (with the exception of Quebec).Full-TimeStaff
Salary164,000 - 248,000 CAD per year
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Job Details
- Experience
- 7+ years in Data Product Management, Data Science, Machine Learning, or a related technical field
- Required Skills
- Machine LearningSnowflakeA/B testingDatabricks
Requirements
- Have 7+ years in Data Product Management, Data Science, Machine Learning, or a related technical field.
- Have a track record of shipping production ML or data products.
- Demonstrate deep fluency in applied ML, including training data, ground truth, loss functions, precision and recall, overfitting, and model evaluation.
- Understand ML platforms and infrastructure, including model serving, feature pipelines, batch versus real-time inference, latency trade-offs, and deployment workflows.
- Have experience partnering directly with Data Scientists and ML Engineers to challenge assumptions and navigate complex dependencies.
- Have experience with modern data and ML tools such as Databricks, Snowflake, AWS, or similar.
- Connect ML work to business outcomes such as revenue growth, customer lifetime value, conversion, and marketing efficiency.
- Demonstrate communication and cross-functional leadership, including influencing technical and business stakeholders without formal authority.
- Have a Bachelor's or advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related field, or equivalent practical experience.
Responsibilities
- Partner with Data Scientists and ML Engineers on modeling trade-offs, ground-truth definitions, training data quality, precision and recall, and feature strategy.
- Navigate ML infrastructure decisions, including model serving, feature pipelines, batch versus real-time inference, latency, and production dependencies.
- Turn ambiguous business problems into model objectives, technical requirements, and measurable success criteria.
- Own and prioritize the ML roadmap across verticals, balancing business impact, technical feasibility, and engineering capacity.
- Work across Data Science, Data Engineering, Product Engineering, and business teams to integrate model predictions into consumer experiences, decisioning systems, and workflows.
- Ensure launches are production-ready, dependencies have clear ownership, and performance is validated against technical and business requirements.
- Work with Data Science and Analytics on experiments, A/B tests, and causal measurement of ML's incremental impact.
- Define and monitor predictive performance, reliability, adoption, and business outcome metrics.
- Explain model performance, trade-offs, limitations, and revenue impact to senior stakeholders.
- Set the long-term vision for ML products and mentor product managers and partners.
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