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Open nowPosted 8 days ago

Staff Data Product Manager (Machine Learning) - Canada

NerdWallet30 open roles

Pay
CA$164,000 – CA$248,000 a year
Where
NerdWallet Canada
Work mode
Remote
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Your applicationOpen nowStaff Data Product Manager (Machine Learning) - CanadaNerdWallet · NerdWallet Canada
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The clock on this job

Early applications get read.

13.9% of postings close within 7 days. Measured by our own scanner across the market. NerdWallet postings stay open a median of 28 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.9%7 days
  4. 22.4%14 days
  5. 38.1%30 days
  6. 59.0%60 days
This job: posted 8 days ago

NerdWallet median: 28 days open

The posting

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 Engineering

- 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 the roadmap and drive models into production

- 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.

Measure what matters

- 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.

Lead beyond your own roadmap

- Set 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, 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, and marketing efficiency

- Excellent communication and cross-functional leadership, including influencing technical and business stakeholders without formal authority

- Bachelor’s or advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related field, or equivalent practical experience

WHERE:

- This is a remote position and a person can be located anywhere in Canada (with the exception of Quebec).

- NerdWallet is proud to be a remote-first company! We believe great work can be done anywhere. No matter where you are based, NerdWallet offers benefits and perks to support the physical, financial, and emotional well being of you and your family.

WHAT WE OFFER:

Work Hard, Stay Balanced (Life’s a series of balancing acts, eh?)

- Monthly Healthcare Stipend

- Rejuvenation Policy – Vacation Time Off + You will receive the official public holidays in your province

- Paid sabbatical for Nerds to recharge, gain knowledge and pursue their interests

- Monthly Wellness Stipend, Wifi Stipend, and Cell Phone Stipend

- Work from home equipment stipend

Have Some Fun! (Nerds are fun, too)

- Nerd-led group initiatives – Employee Resource Groups for Parents, Diversity, and Inclusion, Women, LGBTQIA, and other communities

- Hackathons and team events across all teams and departments

- Company-wide events like NerdLove (employee appreciation) and our annual Charity Auction

Plan for your future (And when you retire on your island, remember the little people)

- RRSP with a 4% match. Eligible one month after hire.

- Financial wellness, guidance, and unlimited access to a Certified Financial Planner (CFP) through Northstar

NerdWallet is committed to pursuing and hiring a diverse workforce and is proud to be an equal opportunity employer. We prohibit discrimination and harassment on the basis of any characteristic protected by applicable federal, state, or local law, so all qualified applicants will receive consideration for employment.

#LI-Remote

#LI-6

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