Wealthsimple

Staff Data Scientist, Transfers

Job Description

The Staff Data Scientist role is part of the Sales Data Science team at Wealthsimple, which focuses on building data products that make the conversion process as easy as possible for our clients, and enable our sales and CXO teams to target, and offer the right products/offerings to client leads to drive conversions.

This individual will leverage deep expertise in operations analytics and modeling, working across large, complex structured and unstructured datasets to identify opportunities to make the account transfer experience simpler, faster, and more reliable for clients, as well as more efficient for our Transfers Operations and Customer Support teams.

You will partner closely with stakeholders across Product, CXO, Operations, and Data to build scalable data products and analytical frameworks that uncover systemic operational failures and root causes, surface high-impact improvement opportunities, and enable proactive, data-driven client engagement and interventions.

As a Staff Data Scientist on the team, you will play a key leadership role in defining the analytical and modeling strategy for transfers. You will lead complex investigations, build scalable data products, and partner with senior stakeholders to turn insights into durable improvements in client experience, operational efficiency, and retention. This role offers the opportunity to own a critical business domain end-to-end and drive meaningful impact at scale, as well as be a technical leader on the data science team.


In this role, you’ll have the opportunity to:
  • Own the end-to-end analytical and modeling strategy for Account Transfers, operating with high autonomy in a complex, ambiguous problem space with material client and business impact.
  • Execute deep investigations across large, complex structured and unstructured datasets to uncover systemic operational failures, root causes, and improvement opportunities within the transfers ecosystem.
  • Design and build scalable data models and analytical frameworks that enable proactive identification of transfer failures, early risk detection for at-risk client transfers and actionable signals for CXO and Product teams.
  • Apply advanced modeling techniques (e.g., clustering, segmentation, predictive modeling, causal analysis) to identify distinct transfer behaviour archetypes, surface institutional-level patterns across external counterparties, and inform differentiated operational and product strategies.
  • Partner closely with Product, CX, Operations, and Engineering leaders to translate analytical insights into product roadmap decisions, operational process design,  and client-facing interventions that measurably improve outcomes.
  • Build and productionize data products (ML models, dashboards, decision tools) that embed analytics directly into workflows and decision-making.
  • Measure and communicate impact, tying analytical work to improvements in transfer success rates, client experience, operational efficiency, and retention.
  • Act as a technical and analytical leader by:
  • Defining best practices for modeling, experimentation, and measurement in the transfers domain.
  • Mentoring senior data scientists and raising the bar for analytical rigor.
  • Influencing data strategy beyond immediate team boundaries.

    • What you bring:
  • 7–10+ years of experience in data science, applied statistics, analytics, machine learning, or operations research and at least 3-5 years in Senior or Staff-level roles.
  • Strong background working with complex, multi-step operational systems (e.g., operations, payments, transfers, risk, fraud, compliance, customer lifecycle, or similar domains).
  • Experience applying operations research techniques such as optimization, simulation, queuing theory, or decision modeling to improve operational outcomes.
  • Deep expertise in statistical analysis, experimentation, and inference on real-world data.
  • Hands-on experience building predictive and prescriptive models (e.g., classification, regression, anomaly detection, routing).
  • Expert SQL skills, including complex joins, window functions, and performance optimization
  • Excellent Python skills for data analysis, modeling, optimization, and production-ready workflows.
  • Experience working with large, messy, and incomplete datasets, including both structured and unstructured data.
  • Excellent communication skills, with experience presenting complex analytical or optimization results to non-technical stakeholders and influencing product strategy, operational decisions or roadmap priorities.
  • Track record of driving measurable business or client impact through data science and/or operations research work.
  • Experience mentoring or coaching senior data scientists and raising analytical standards within a team.
  • Comfortable operating with high autonomy and making judgment calls under uncertainty

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