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NerdWallet

Personal finance guidance with product comparisons

Senior Data Scientist - Applied ML

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in Canada

About the Job

Requirements

  • At least 5 years of professional experience in Data Science, Applied Machine Learning, or a related field, with a demonstrated track record of developing and deploying machine-learning solutions that drive measurable business impact.
  • Deep, hands-on Databricks experience, including building and managing production machine-learning workflows using MLflow and the Databricks ML ecosystem; experience with similar platforms is not a substitute.
  • Advanced proficiency in Python and SQL, and extensive experience with 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.
  • Ability to independently lead complex, ambiguous technical initiatives from problem definition through deployment and business-impact measurement.
  • Strong business acumen and ability to connect technical solutions to measurable outcomes such as revenue growth, conversion improvement, customer lifetime value, and marketing efficiency.
  • Bachelor's or advanced degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline, or equivalent practical experience.

Responsibilities

  • Lead end-to-end development of advanced machine-learning models focused on customer lifetime value, 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.
  • Develop recommendation and ranking models that identify relevant financial products for individual consumers based on their needs, behaviors, and predicted propensity to engage or convert.
  • Explore and implement propensity modeling, learning-to-rank, and next-best-product recommendation approaches to improve customer engagement and cross-sell opportunities.
  • Collaborate with Product and Marketing to integrate predictive intelligence into consumer experiences and personalized communications.
  • Use Databricks and MLflow to develop, train, deploy, monitor, and manage machine-learning models at scale.
  • Establish data-science practices for 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.
  • Mentor other practitioners, influence architectural decisions, and elevate engineering and modeling standards across the organization.

About the Company

NerdWallet

Personal finance guidance with product comparisons

NerdWallet is a financial guidance platform that helps people compare and understand financial products. It offers tools and resources for credit cards, mortgages, personal loans, insurance, and debt options, plus calculators and educational content to simplify complex money topics. Users access detailed product comparisons and expert advice, while the site earns revenue through affiliate marketing by earning commissions when users sign up for products via recommendations. This model lets the service be free for users while maintaining trust and transparency. NerdWallet’s goal is to help consumers make smarter financial decisions by providing clear information and practical tools.

Company size
1,001-5,000
Company stage
IPO
Headquarters
San Mateo, CA
Founded
2009

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