Senior Data Scientist – Applied ML

NerdWallet

. Lead end-to-end development of machine learning models for customer lifetime value, conversion propensity, lead quality, and revenue optimization .

Posted 11/4/2026full-timeRemote • CanadaSenior💰 CA$161,000 - CA$243,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and deploying machine learning models, with a strong focus on customer lifetime value and revenue optimization. Proficient in translating complex model predictions into actionable marketing strategies while ensuring model performance and reliability.

Highest-signal resume keywords

Machine Learning Model DevelopmentDatabricks and MLflow ProficiencyAdvanced Python and SQL SkillsExperience with Scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlowStrong Business Acumen

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPredictive ModelingFeature EngineeringModel Evaluation TechniquesBatch and Real-Time Inference SystemsVersion ControlCI/CD PracticesData Drift MonitoringExperiment DesignAutomated Training Pipelines

Soft Skills

Excellent CommunicationCollaboration SkillsInfluencing Technical DirectionMentoringProblem-Solving

Tools & Technologies

DatabricksMLflowPythonSQLScikit-learnXGBoostLightGBMPyTorchTensorFlow

Industry Keywords

Data ScienceApplied Machine LearningCustomer AcquisitionMarketing Investment DecisionsFinancial Product Recommendations

Tech Stack

Tools & technologies

PythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Lead end-to-end development of machine learning models for customer lifetime value, conversion propensity, lead quality, and revenue optimization
Translate model predictions into signals improving marketing investment decisions, customer acquisition efficiency, and long-term profitability
Develop recommendation and ranking models for personalized financial product recommendations
Implement propensity modeling, learning-to-rank, and next-best-product recommendations
Integrate predictive intelligence into consumer experiences and personalized communications with Product and Marketing
Use Databricks and MLflow to develop, train, deploy, monitor, and manage models at scale
Establish experiment tracking, model versioning, feature engineering, automated training pipelines, model monitoring, and reproducibility practices
Define standards for model performance, reliability, retraining, and production readiness
Monitor data drift and model degradation
Design and evaluate experiments measuring incremental business impact
Mentor practitioners, influence architectural decisions, and elevate engineering and modeling standards
Partner with Data Engineering, Product, Marketing, and Analytics to translate business problems into scalable ML solutions

Requirements

What you’ll need

5+ years of professional experience in Data Science, Applied Machine Learning, or a related field
Hands-on Databricks experience building and managing production machine learning workflows using MLflow and the Databricks ML ecosystem; similar platforms are not a substitute
Advanced proficiency in Python and SQL
Extensive experience with scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow
Strong understanding of supervised learning, predictive modeling, classification, regression, ranking, and model evaluation techniques
Experience designing scalable batch and/or real-time inference systems
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 connecting technical solutions to measurable outcomes
Excellent communication and collaboration skills; experience influencing technical direction and working with senior stakeholders and cross-functional teams
Bachelor's or advanced degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline, or equivalent practical experience
Must be located anywhere in Canada except Quebec
Must be authorized to work in Canada

Benefits

Comp & perks

Monthly Healthcare Stipend
Rejuvenation Policy – Vacation Time Off + official public holidays in your province
Paid sabbatical
Monthly Wellness Stipend
Wifi Stipend
Cell Phone Stipend
Work from home equipment stipend
Employee Resource Groups
Hackathons and team events
Company-wide events
RRSP with a 4% match, eligible one month after hire
Financial wellness guidance and unlimited access to a Certified Financial Planner (CFP) through Northstar