Wealthsimple Technologies

Sr Data Scientist, AI Products

Wealthsimple Technologies3 weeks ago
Location

Remote (Canada)

Workplace

Remote

Type

Full Time

Salary

CAD 151,000 – 189,000

Level

Senior

Role

ML Engineer

Posted

Jul 3, 2026

Full TimeRemoteSenior

The role

Summary

Senior Data Scientist role at Wealthsimple focusing on AI products development for a financial platform serving 4+ million Canadians. This full-stack position requires leading the complete AI lifecycle from model architecture and data pipeline engineering to experimentation and production deployment, with emphasis on translating AI breakthroughs into tangible product experiences that drive measurable business outcomes.

What you'll do

Lead Full AI Product Lifecycle Management: Own end-to-end AI initiatives from identifying user pain points and establishing product KPIs through model architecture design, training foundation models, and measuring real-world business impact for Wealthsimple's 4+ million user base across financial products.
Design and Execute Advanced Experiments: Design and execute rigorous statistical experiments including A/B tests and causal inference analyses to validate new AI features, optimize model performance, and drive evidence-based product improvements that maximize user value.
Cross-Functional Product Translation: Partner with Product Managers, UX Designers, and Backend Engineers to translate business objectives into technical AI roadmaps, ensuring model development aligns with user needs, technical feasibility, and product strategy while maintaining open communication with all stakeholders.
Build Production-Grade Data Pipelines: Engineer and maintain scalable training pipelines, evaluation harnesses, and model serving infrastructure using reproducible, well-tested code that follows software engineering best practices for reliability and maintainability in production environments.
Iterate Based on Production Feedback: Monitor production model performance, analyze user interactions and system telemetry, and systematically refine models based on real-world feedback to continuously maximize product value and user satisfaction metrics.
Foster Collaborative Team Culture: Mentor junior data scientists and engineers, communicate complex ML concepts to non-technical stakeholders, simplify technical problems into manageable components, and drive iterative, high-impact delivery through collaborative problem-solving.
Product Analytics and Insights: Leverage product analytics techniques to derive actionable insights from user data, inform product roadmaps, define success metrics, and translate vague business requirements into measurable hypotheses that guide model development priorities.

What we look for

Technical

Deep Learning and Transformer ArchitecturesProven expertise designing, training, and deploying production AI models with advanced knowledge of transformer architectures, Large Language Models (LLMs), and modern deep learning frameworks for financial applications.
Machine Learning FundamentalsStrong foundational knowledge of attention mechanisms, tokenization, embedding techniques, regularization strategies, and failure mode analysis essential for building robust and reliable AI systems.
Model Optimization TechniquesHands-on experience with model fine-tuning approaches including LoRA and adapter methods, knowledge distillation for model compression, quantization for deployment efficiency, and instruction tuning for task-specific optimization.
Experimentation and Statistical AnalysisAdvanced proficiency in designing and executing A/B tests, causal inference methodologies, and statistical hypothesis testing to rigorously validate AI features and measure product impact with statistical significance.
Production ML Systems EngineeringExperience building scalable machine learning infrastructure including training pipelines, model evaluation frameworks, and deployment systems that prioritize reproducibility, monitoring, and maintainability in production environments.
Data Manipulation and AnalysisExpert-level proficiency with data processing libraries and SQL for extracting insights from large-scale datasets, feature engineering, and exploratory data analysis to inform model development decisions.

Education

Bachelor's Degree in Technical FieldBachelor's degree in Computer Science, Machine Learning, Statistics, Mathematics, Physics, or related technical discipline. Advanced degree (Master's or PhD) in ML, AI, or related field is a strong plus.

Experience

Senior-Level Production ML ExperienceMinimum 5+ years of professional experience in machine learning and data science roles, with substantial time spent shipping production models that directly impact business metrics and user experiences.
Product-Minded AI DevelopmentProven track record translating model outputs into measurable business outcomes, with demonstrated ability to communicate technical findings to cross-functional partners including Product Managers, Designers, and Engineers.
LLM Application DevelopmentHands-on experience developing and deploying Large Language Model applications in production environments, including prompt engineering, fine-tuning, and optimization for real-world financial or consumer-facing use cases.
Product Analytics IntegrationExperience leveraging product analytics to drive insights that inform model development, with ability to define success metrics, establish baselines, and measure feature impact through rigorous experimentation.

Skills

Required skills

Python ProgrammingExpert-level Python proficiency for model development, data processing, and building scalable machine learning systems with clean, maintainable code following software engineering best practices.
PyTorch or TensorFlowDeep expertise with modern deep learning frameworks (PyTorch preferred for research flexibility, or TensorFlow for production stability) for implementing transformer models and LLM applications.
SQL and Data QueryingAdvanced SQL skills for querying large-scale databases, performing complex joins and aggregations, and extracting features for model training and analysis.
Statistical Testing and Causal InferenceProficiency in statistical hypothesis testing, A/B testing design, and causal inference methodologies (propensity score matching, instrumental variables) for rigorous experimentation.
Model Evaluation and MetricsDeep understanding of model evaluation frameworks, appropriate metrics selection for different tasks, and techniques for measuring real-world product impact beyond traditional ML metrics.
Communication and CollaborationExceptional ability to translate complex machine learning concepts into actionable insights for non-technical stakeholders, document technical decisions, and work effectively within cross-functional teams.

Nice to have

Recommender Systems ExperienceBackground developing recommendation algorithms, collaborative filtering systems, or personalization engines that optimize for user engagement and business outcomes in consumer applications.
Representation LearningAdvanced knowledge of embedding techniques, representation learning, and self-supervised learning methodologies for learning meaningful feature representations from unlabeled data.
Self-Supervised LearningExperience implementing self-supervised learning approaches for pre-training models or learning from unlabeled data to improve model robustness and reduce annotation requirements.
Open-Source ContributionsActive contributions to popular open-source machine learning projects (PyTorch, Hugging Face, scikit-learn) demonstrating commitment to the broader ML community and software engineering excellence.
Research PublicationsPublished research papers in peer-reviewed machine learning conferences or journals (NeurIPS, ICML, ICLR) showing leadership in advancing state-of-the-art AI techniques.
FinTech or Financial Domain KnowledgePrior experience in financial technology, wealth management, or financial services industries, bringing domain expertise in regulatory considerations and financial product development.
MLOps and Deployment InfrastructureHands-on experience with MLOps tooling (DVC, Weights & Biases, MLflow), containerization (Docker, Kubernetes), and CI/CD pipelines for automating model training and deployment workflows.

Compensation & benefits

Salary

CAD 151,000 – 189,000 (annual)

Stock options

Available

Benefits

Comprehensive Health and Wellness Coverage

Top-tier health benefits including medical, dental, and vision coverage, plus life insurance providing security and peace of mind for you and your family.

Long-Term Financial Security

Group savings program with employer matching through Wealthsimple for Business, enabling you to build wealth long-term while working at a financial technology company.

Generous Paid Time Off

20 vacation days annually plus 4 wellness days specifically designated for self-care and professional development, plus unlimited sick and mental health days demonstrating genuine commitment to employee wellbeing.

Remote Work Flexibility

Ability to work outside Canada for up to 90 days per year, offering flexibility for remote work, travel, or exploring work arrangements that support your lifestyle while maintaining productivity.

Diversity and Inclusion Programs

Active employee resource groups including Rainbow (2SLGBTQ+), Women of WS, and Black at WS fostering an inclusive culture where diverse perspectives are valued and celebrated.

Collaborative Hybrid Work Environment

Hybrid work model with access to modern offices and collaboration tools, enabling flexibility while maintaining connection with 1,500+ talented team members across North America.

Professional Development

Access to learning resources, conferences, and development opportunities supporting continuous growth in machine learning, AI, and engineering excellence throughout your career.

Interview process

  1. 1
    Application and Resume Review — Initial screening of applications and resumes with potential AI-assisted analysis tools supporting human hiring team members. Final decisions remain made by experienced recruiters and hiring managers evaluating your background, relevant experience, and technical qualifications.
  2. 2
    Recruiter Phone Screening — Conversation with Wealthsimple recruiter covering your background, motivation for the role, understanding of Wealthsimple's mission, and discussion of compensation expectations to ensure alignment before proceeding to technical interviews.
  3. 3
    Technical Case Study or Take-Home Assignment — Machine learning project or case study testing your problem-solving approach, model design thinking, and ability to articulate technical decisions. This evaluation assesses practical ML skills and communication of technical reasoning.
  4. 4
    Technical Interview with Data Scientists — In-depth conversation with senior members of the AI Products team covering machine learning concepts, deep learning architectures, experimentation design, production ML systems, and discussion of your past projects and technical decision-making processes.
  5. 5
    Product and Collaboration Interview — Conversation with Product Manager or Design partner evaluating your product thinking, cross-functional collaboration skills, ability to translate between technical and business concepts, and alignment with the team's product-driven culture.
  6. 6
    Leadership and Culture Fit Interview — Discussion with senior leadership or team lead assessing your mentorship potential, communication style, collaboration approach, alignment with Wealthsimple's values of building inclusive products, and ability to thrive in the company culture.
  7. 7
    Final Offer and Negotiation — Offer presentation including compensation details, equity/stock options, benefits overview, and discussion of role expectations, team structure, and growth opportunities within Wealthsimple's AI Products organization.
Apply for this position

You'll be redirected to the company's application page

Wealthsimple Technologies

Wealthsimple Technologies

View all jobs

Wealthsimple Technologies is a Canadian financial technology company that provides automated investing and personal finance management solutions to consumers. The company offers a range of digital wealth management products, including robo-advisor investment accounts, tax-free savings accounts, and financial planning tools designed to make investing accessible and affordable for everyday people. Wealthsimple operates in the fintech and wealth management industry, serving millions of users across multiple countries with a mission to democratize access to financial services.

Toronto, CanadaFounded 2014wealthsimple.com/en-ca

Tech Stack

Languages
Python
Frameworks
PyTorchTensorFlowHugging Face TransformersScikit-learn
Databases
PostgreSQLBigQuery or Snowflake
Tools
Git and GitHubJupyter NotebooksWeights & Biases or MLflowDocker
Other
Cloud Platforms (AWS, GCP, or Azure)Apache SparkLLM APIs and Prompt Engineering
Apply Now

On this page