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 Growth

- Lead 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 Systems

- Develop 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 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.

- Define standards for model performance, reliability, retraining,



and production readiness, including monitoring for data drift and model degradation.

- Design and evaluate experiments to measure the incremental business impact of deployed models and continuously improve their effectiveness.

- Serve as a technical leader within Data Science, mentoring other practitioners, influencing architectural decisions, and elevating engineering and modeling standards across the organization.

Your experience:

- 5+ years of qualified experience in Data Science, Applied Machine Learning, or a related field, with a demonstrated track record of developing and deploying ML solutions that drive measurable business impact.

- Deep, hands-on experience with Databricks, including building and managing production machine learning workflows using MLflow and the Databricks ML ecosystem. Please note: this role requires hands-on Databricks experience specifically, and experience with similar platforms is not a substitute.

- Advanced proficiency in Python and SQL, with extensive experience using modern machine learning frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.

- Strong understanding of supervised learning, predictive modeling, classification, regression, ranking, and model evaluation techniques.

- Practical experience designing scalable batch and/or real-time inference systems, with an understanding of latency, reliability, and performance trade-offs.

- Strong software engineering fundamentals, including version control, testing, modular code design, and CI/CD practices for machine learning.

- Demonstrated ability to independently lead complex, a

📌 Senior Data Scientist (Canada)
🏢 NerdWallet
📍 Canada