Data Product Manager; Machine Learning

Nerd Wallet US

Full time

Remote

Data

US $140K – $246K Offers Equity

Base pay offered may vary within the posted range based on several factors, including but not limited to education, job-related knowledge, skills, experience, and location.

Nerd Wallet’s mission is to provide clarity for all of life’s financial decisions. Machine learning is how we get there  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 Nerd Wallet. 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.

Dig 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 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.

Work 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.

Set the long-term vision for ML products, influence without formal authority, and mentor other product managers and partners.

• 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, feature pipelines, batch vs. real-time inference, latency trade-offs, and deployment workflows
• Experience partnering directly with Data Scientists and ML Engineers to challenge assumptions and navigate complex dependencies
• Experience with modern data and ML tools such as Databricks, Snowflake, AWS, or similar
• Strong business sense, with the ability to connect ML work to revenue growth, customer lifetime value, conversion,…