Associate, Credit Card

Build something people love

Wealthsimple is on a mission to help everyone achieve financial freedom by reimagining what it means to manage your money. Using smart technology, we take financial services that are often confusing and expensive and make them transparent and low-cost for everyone. We’re the largest fintech company in Canada, with 3+ million users who trust us with more than $100 billion in assets.

And we’re just getting started.

We’re looking for someone who thinks big, sweats the small stuff and keeps things simple. You’ll join a high-bar, fast-paced team where people are trusted to own their work, communicate openly, and ship things that improve our clients’ lives. Collaboration, humility, and an obsession over quality are how we get stuff done.

At Wealthsimple, we aren’t just managing portfolios; we’re rewriting the rules of how Canadians interact with credit. The Credit Analytics team isn't your typical lending department. We are the owners of Wealthsimple’s retail lending ecosystem, operating at the intersection of high-growth tech and data driven underwriting. We believe that being "credit-worthy" should mean more than just a traditional score. Our mission is to challenge the status quo, building inclusive products that empower millions of Canadians to lead better financial lives.

We are currently in a high-growth "green-field" phase, moving from small data to massive scale through iteration and experimentation.

We are hiring an Associate who can collaborate cross-functionally with our Engineering, Product, Data Science, Fraud, and Client Experience teams. You will contribute to strategies for credit card approval, initial limits, line management, pricing, authorization, and collections across a growing number of credit card products and customer segments. The Credit Analytics team is a great place to see your work deliver significant business impact and accelerate Wealthsimple's goal to be the primary financial service provider to millions of Canadians.

• Support the development of strategies to manage risk and optimize performance across Wealthsimple’s credit card products, and other retail lending domains.
• Evaluate the performance of lending portfolios, and the performance of your campaigns, lending strategies and experiments.
• Recommend strategy enhancements to improve marginal profitability and mitigate emerging risks.
• Execute and analyze retail lending campaigns that drive revenue by changing approvals, line management, pricing, credit limits, recovery rates.
• Work across many teams to support road maps, and implement your strategies that drive immediate impact.

• Has strong data skills: dashboards, reporting, visualization, data manipulation.
• Experience using SQL to conduct quantitative analysis, and synthesizing results into actionable proposals.
• Can develop and communicate stories to both technical and non-technical stakeholders. Is able to internalize and weigh information to come to balanced decisions.
• Is intellectually curious, passionate about their work, and wants to have ownership over their projects.
• Has 1-2 years experience in a retail lending domain such as credit risk, credit modeling, credit lifecycle management, collections, credit policy implementation, profitability analysis. Co-op experience accepted.
• Experienced with statistical/quantitative analysis: experimental design, champion/challenger (or A/B) testing, model accuracy validation, constrained optimization, decision trees, discounted cash flows, financial modeling.
• Has technical experience in: SQL, Python, Git Hub.
• Airflow, AWS redshift, dbt are nice-to-have.

🤑 Base salary range:
For this role, candidates located in Canada can expect a base salary range of CAD $100,000 - $125,000. Actual compensation is determined based on skills, experience, and role level. Exceptional candidates may be considered above the top of the range, and pay can increase quickly for those who make a big impact in the role.

🚀 Total compensation:
In addition to base salary, this role includes equity compensation. We use clear job levels and…