Senior Data Scientist (Applied ML) - Canada

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

Job summary

NerdWallet is looking for a Senior Data Scientist (Applied ML) to join our growing team. You will lead production‑grade machine learning solutions that power customer personalization, predictive modeling, and performance marketing, with a direct impact on how we acquire, engage, and monetize our customer relationships.

You will collaborate with Data Engineering, Product, Marketing, and Analytics to translate complex business problems into scalable ML systems, while helping establish technical

Responsibilities

• Build Predictive Models That Drive Business Growth
• Translate model predictions into actionable signals that improve marketing investment decisions, customer acquisition efficiency, and long-term profitability.
• Advance Personalization Recommendation Systems
• Explore and implement approaches such as propensity modeling, learning-to-rank, and next-best-product recommendations to improve customer engagement and cross-sell opportunities.
• Collaborate with Product and Marketing to integrate predictive intelligence into consumer experiences and personalized communications.
• Establish Best-in-Class ML Practices—Leverage Databricks and MLflow to develop, train, deploy, monitor, and manage machine learning models at scale.
• Establish robust DS practices, including experiment tracking, model versioning, feature engineering, automated training pipelines, model monitoring, and reproducibility.

Job description

At NerdWallet, our mission is to provide clarity for all of life's financial decisions. Data Science plays a critical role in delivering on that mission, helping us better understand our consumers, personalize their experiences, and connect them with the financial products that best meet their needs.We’re growing, and we’re looking for a Senior Data Scientist (Applied ML) to join us as a foundational member of our new team. You’ll help shape and advance our machine learning capabilities across consumer personalization, predictive modeling, and performance marketing, with a real voice in how we work from day one. Together, we’re building an inclusive, supportive team culture where we lift each other up and succeed together to drive business impact across the organization.In this role, you will lead the development of production‑grade machine learning solutions that power some of NerdWallet's most critical business decisions. From predicting customer lifetime value and scoring incoming leads in real time to developing recommendation engines that connect consumers with the right financial products, your work will have a direct impact on how we acquire, engage, and monetize our customer relationships.This is a hands‑on individual contributor role for someone with deep expertise in applied machine learning, production ML systems, and the Databricks ecosystem. As a foundational member of the team, you’ll partner closely with Data Engineering, Product, Marketing, and Analytics to translate complex business problems into scalable ML solutions, while helping establish the technical standards and best practices for machine learning across NerdWallet.

Where you can make an impact:

• Build Predictive Models That Drive Business GrowthLead the end-to-end development of advanced machine learning models focused on customer lifetime value (LTV), conversion propensity, lead quality, and revenue optimization.
• Translate model predictions into actionable signals that improve marketing investment decisions, customer acquisition efficiency, and long-term profitability.
• Advance Personalization Recommendation SystemsDevelop recommendation and ranking models that identify the most relevant financial products for individual consumers based on their needs, behaviors, and predicted propensity to engage or convert.
• Explore and implement approaches such as propensity modeling, learning-to-rank, and next-best-product recommendations to improve customer engagement and cross-sell opportunities.
• Collaborate with Product and Marketing to integrate predictive intelligence into consumer experiences and personalized communications.
• Establish Best-in-Class ML PracticesLeverage Databricks and MLflow to develop, train, deploy, monitor, and manage machine learning models at scale.
• Establish robust DS practices, including experiment tracking, model versioning, feature engineering, automated training pipelines, model monitoring, and reproducibility.
• Define standards for model performance, reliabi