Senior Data Scientist (Applied ML)

Accountabilities

Lead the end-to-end development, deployment, and optimization of advanced machine learning models focused on customer lifetime value (LTV), conversion propensity, lead quality, and revenue optimization.
Translate predictive model outputs into actionable business insights that improve marketing investment decisions, customer acquisition efficiency, and long-term profitability.
Design, develop, and implement recommendation and ranking systems that match consumers with relevant financial products based on their individual needs, behaviors, and likelihood to engage or convert.
Explore and apply advanced machine learning approaches, including propensity modeling, learning-to-rank, and next-best-product recommendation systems, to improve customer engagement and cross-selling opportunities.
Collaborate with Product and Marketing teams to integrate predictive intelligence into consumer-facing experiences, personalized communications, and customer acquisition strategies.
Leverage Databricks and MLflow to build, train, deploy, monitor, and manage production-grade machine learning models and workflows at scale.
Establish and maintain robust data science practices, including experiment tracking, model versioning, feature engineering, automated training pipelines, monitoring, and reproducibility.
Define and implement technical standards for model performance, reliability, retraining, and production readiness, including monitoring for data drift and model degradation.
Design and conduct experiments to measure the incremental business impact of deployed models and continuously improve their effectiveness.
Develop scalable batch and real-time inference solutions, balancing latency, reliability, performance, and business requirements.
Take ownership of complex technical initiatives, from initial problem definition and solution design through deployment, monitoring, and business impact measurement.
Act as a technical leader within the Data Science function by mentoring fellow practitioners, influencing architectural decisions, and strengthening machine learning engineering and modeling standards.
Partner with cross-functional stakeholders to ensure machine learning solutions address real business needs and deliver measurable improvements in revenue, conversion, customer lifetime value, and marketing efficiency.
Requirements
At least 5 years of professional experience in Data Science, Applied Machine Learning, or a related discipline, with a proven track record of developing and deploying machine learning solutions that generate measurable business results.
Extensive, hands-on experience with Databricks, including building and managing production machine learning workflows using MLflow and the broader Databricks ML ecosystem. Direct Databricks experience is essential; experience with comparable platforms alone will not be considered a substitute.
Advanced proficiency in Python and SQL, with substantial 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 algorithms, and model evaluation methodologies.
Practical experience designing, developing, and deploying scalable batch and/or real-time inference systems, with a solid understanding of latency, reliability, and performance trade-offs.
Strong software engineering fundamentals, including version control, automated testing, modular code design, and CI/CD practices for machine learning applications.
Demonstrated ability to independently lead complex and ambiguous technical projects, from defining business problems and selecting appropriate approaches to deploying solutions and measuring their impact.
Strong business acumen and the ability to connect technical initiatives to measurable commercial outcomes, including revenue growth, conversion improvement, customer lifetime value, and marketing efficiency.
Excellent communication and collaboration skills, with the ability to explain complex technical concepts, influence technical direction, and work effectively with senior stakeholders and cross-functional teams.
Bachelor's or advanced degree in Computer Science, Statistics, Mathematics, Engineering, or another quantitative discipline, or equivalent practical experience.
Strong problem-solving abilities, analytical thinking, and a proactive approach to identifying opportunities for innovation and continuous improvement.
Ability to work independently while contributing to a supportive, collaborative, and geographically distributed team environment.
Benefits
Competitive annual base salary ranging from CA$161,000 to CA$243,000, depending on qualifications, relevant experience, skills, and location.
Fully remote, remote-first working environment, with the flexibility to work from anywhere in Canada, excluding Quebec.
Monthly healthcare stipend to support personal and family well-being.
Vacation time off through a rejuvenation policy, in addition to official public holidays in your province.
Paid sabbatical opportunities to recharge, pursue personal interests, and expand professional knowledge.
Monthly wellness stipend to support physical and emotional well-being.
Monthly Wi-Fi and cell phone stipends.
Work-from-home equipment stipend to help create a productive remote workspace.
Retirement savings plan (RRSP) with a 4% employer match, available one month after hiring.
Access to financial wellness resources, personalized guidance, and unlimited consultations with a Certified Financial Planner (CFP) through Northstar.
Employee Resource Groups supporting parents, diversity and inclusion, women, LGBTQIA+ communities, and other employee communities.
Opportunities to participate in hackathons, team-building activities, and cross-departmental events.
Company-wide initiatives, including employee appreciation events and an annual charity auction.
Inclusive and collaborative working culture that encourages professional development, innovation, and meaningful contributions.