Data Scientist, Growth (12 month contract)

EQ Bank | Canada's Challenger Bank; Toronto; Hybrid; CAD 120,000 - 180,000; Full time

Benefits offered by this job

Competitive discretionary bonus

Market leading RRSP match program

Medical, dental, vision, life, and dis

Employee Share Purchase Plan

Maternity/Parental top-up

Generous vacation policy and personal

Virtual events to connect with fellow

Career development program

Fulfilling opportunity at a top FinTec

Job summary

EQ Bank is hiring a senior Data Scientist for Growth to lead ML-driven initiatives across pricing, engagement, and decisioning. You will design scalable pipelines, develop experimentation frameworks, and mentor teammates while accelerating model deployment across cloud platforms.

The role requires 6+ years of data science experience, strong Python/SQL, and proven production ML experience in cloud environments. Hybrid work from Toronto area preferred.

Qualifications

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Economics, Engineering, or a related quantitative field.
• Six or more years of total experience in data science, machine learning, advanced analytics, or a closely related discipline.
• Strong proficiency in Python, Structured Query Language, statistical analysis, Spark or similar distributed technologies, and scalable data processing.
• Experience building, validating, deploying, monitoring, and governing machine learning models in production or production-adjacent environments.
• Strong understanding of experimental design, hypothesis testing, causal inference, and measurement frameworks.
• Experience with cloud analytical environments such as Google Cloud Platform, Microsoft Azure, Microsoft Fabric, Amazon Web Services, or similar platforms.
• Working knowledge of Git-based development, code review, automated testing, dependency management, and continuous integration and deployment.
• Experience with customer growth, personalization, pricing, recommendation systems, retention, or similar business applications.
• Advanced analytical problem-solving and sound judgment in ambiguous situations.
• Ability to explain technical information, trade-offs, migration risks, and insights to technical and non-technical audiences.
• Ability to work independently, manage multiple priorities, and deliver high-quality work in a fast-paced environment.
• Preferred: experience with cloud migrations, Vertex AI or equivalent capabilities, modern machine learning platforms, Docker, infrastructure as code, observability, orchestration, and automated retraining.

Responsibilities

• Lead machine learning model development for growth initiatives, including pricing, customer engagement, recommendation systems, propensity modelling, net promoter score, and related decisioning use cases.
• Design and maintain scalable data pipelines, feature engineering frameworks, and workflows for model training, scoring, monitoring, backtesting, and reporting.
• Develop and evaluate experimentation frameworks, including A/B testing, holdout analysis, uplift measurement, causal inference, and incrementality measurement.
• Perform feature engineering, model selection, validation, calibration, and performance monitoring.
• Partner with product, engineering, analytics, marketing, and business teams to define success measures, prioritize opportunities, and operationalize model outputs.
• Lead or contribute to migration and modernization of models, analytical assets, pipelines, and workflows, including the transition from Microsoft Fabric and Azure to Google Cloud Platform.
• Assess model dependencies and recommend whether assets should be migrated, refactored, rebuilt, consolidated, or retired.
• Design production-ready machine learning solutions, including deployment pipelines, automated retraining, monitoring, and performance tracking.
• Apply software engineering, data engineering, and machine learning operations practices, including modular design, version control, testing, automation, reproducibility, documentation, and code quality.
• Validate migrated or rebuilt solutions by reconciling data, features, model results, and business outputs.
• Communicate model and experiment findings to technical and non-technical stakeholders.
• Create reusable analytical frameworks, shared code libraries, notebooks, and production-ready assets.
• Mentor team members and support knowledge transfer and analytical excellence.

Skills

Python

SQL

Spark

Machine Learning

Experimentation

Cloud Platforms

Communication

Data Visualization

Education

Bachelor's or Master's in Data Science, Statistics, Computer Science, Economics, Engineering, or related field

Tools

Git

Docker

Kubernetes

Google Cloud Platform (GCP)

BigQuery

Job description

Join a Challenger

At EQ, we're remaking banking so every Canadian gets ahead, every day. Serving nearly 4 million Canadians from coast to coast, we offer a wide variety of financial services from banking and lending, to trust and credit union solutions.

We've been at this since 1970, challenging the conventions of traditional banking with smarter, faster, and more connected financial experiences.

What's kept us moving?

The people behind it all: challengers who ask better questions, push back on old assumptions, and look for a better way forward.

If you're driven to help reshape how banking works for Canadians and the businesses that power our economy, this could be your next big opportunity.

We can’t wait to get to know you!

The Work

The Data Scientist, Growth leads the design, development, deployment, and operationalization of advanced analytics and machine learning solutions that support growth, customer experience, and commercial decision-making. The role translates ambiguous business problems into measurable data science solutions, with a focus on pricing, customer engagement, recommendation systems, experimentation, and model-driven decision support.

The role works across the full analytical lifecycle, from problem framing and exploratory analysis through model development, validation, deployment, monitoring, and production implementation. It also contributes to the modernization, migration, and scaling of machine learning assets and data workflows across cloud platforms and enterprise analytical environments.

The Core Responsibilities

• Lead machine learning model development for growth initiatives, including pricing, customer engagement, recommendation systems, propensity modelling, net promoter score, and related decisioning use cases.
• Design and maintain scalable data pipelines, feature engineering frameworks, and workflows for model training, scoring, monitoring, backtesting, and reporting.
• Develop and evaluate experimentation frameworks, including A/B testing, holdout analysis, uplift measurement, causal inference, and incrementality measurement.
• Perform feature engineering, model selection, validation, calibration, and performance monitoring.
• Partner with product, engineering, analytics, marketing, and business teams to define success measures, prioritize opportunities, and operationalize model outputs.
• Lead or contribute to migration and modernization of models, analytical assets, pipelines, and workflows, including the transition from Microsoft Fabric and Azure to Google Cloud Platform.
• Assess model dependencies and recommend whether assets should be migrated, refactored, rebuilt, consolidated, or retired.
• Design production-ready machine learning solutions, including deployment pipelines, automated retraining, monitoring, and performance tracking.
• Apply software engineering, data engineering, and machine learning operations practices, including modular design, version control, testing, automation, reproducibility, documentation, and code quality.
• Validate migrated or rebuilt solutions by reconciling data, features, model results, and business outputs.
• Communicate model and experiment findings to technical and non-technical stakeholders.
• Create reusable analytical frameworks, shared code libraries, notebooks, and production-ready assets.
• Mentor team members and support knowledge transfer and analytical excellence.

Let’s Talk About You

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Engineering, or a related quantitative field.
• Six or more years of total experience in data science, machine learning, advanced analytics, or a closely related discipline.
• Strong proficiency in Python, Structured Query Language, statistical analysis, Spark or similar distributed technologies, and scalable data processing.
• Experience building, validating, deploying, monitoring, and governing machine learning models in production or production-adjacent environments.
• Strong understanding of experimental design, hypothesis testing, causal inference, and measurement frameworks.
• Experience with cloud analytical environments such as Google Cloud Platform, Microsoft Azure, Microsoft Fabric, Amazon Web Services, or similar platforms.
• Working knowledge of Git-based development, code review, automated testing, dependency management, and continuous integration and deployment.
• Experience with customer growth, personalization, pricing, recommendation systems, retention, or similar business applications.
• Advanced analytical problem-solving and sound judgment in ambiguous situations.
• Ability to explain technical information, trade-offs, migration risks, and insights to technical and non-technical audiences.
• Ability to work independently, manage multiple priorities, and deliver high-quality work in a fast-paced environment.
• Preferred: experience with cloud migrations, Vertex AI or equivalent capabilities, modern machine learning platforms, Docker, infrastructure as code, observability, orchestration, and automated retraining.

What we offer [For full-time permanent roles]

• Competitive discretionary bonus
• Market leading RRSP match program
• Medical, dental, vision, life, and disability benefits
• Employee Share Purchase Plan
• Maternity/Parental top-up while you care for your little one
• Generous vacation policy and personal days
• Virtual events to connect with your fellow colleagues
• Professional development and comprehensive Career Development program
• A fulfilling opportunity to join one of the top FinTechs and help create a new kind of banking experience

The incumbent will be working hybrid and in office time will be spent working from EQ Bank’s additional office space located at 2200-25 Ontario Street, Toronto, ON.

Equity, Diversity & Inclusion

EQ is committed to building an inclusive, accessible environment where every employee feels valued, respected, and supported. We believe our organization is stronger — and our people thrive — when we honour and celebrate diverse experiences, identities, and perspectives. We’re equally committed to supporting your growth, both professionally and personally.

We provide a barrier‑free recruitment process and work environment. If you require accommodations at any stage, we will work with you to ensure you can bring your best self to the process and beyond.

As part of our recruitment process, EQ uses AI to help screen, assess, and/or select applicants for this position. All AI-enabled outputs are reviewed and validated by our talent team. All candidates considered for hire must successfully complete a criminal background check and credit check. While we appreciate every application, an EQ recruiter will contact only those whose skills and experience most closely match the requirements of the role.

EQB Inc. (TSX: EQB) is the parent company of Equitable Bank, the country's seventh-largest Schedule I bank by assets, which operates EQ Bank, Canada's Challenger Bank™. EQB Inc. serves nearly 4 million Canadians and manages approximately $150 billion in combined assets under management and administration.

To learn more, visit eqb.investorroom.com and eqbank.ca.