Analytics Developer, Fraud and Anti-Money Laundering

As an Analytics Developer at Wealthsimple, you will be integral to our Fraud and Anti-Money Laundering efforts. Your responsibilities include transforming data into actionable metrics to drive strategies, collaborating with stakeholders to solve their data needs, and implementing robust data controls. Your work directly impacts our ability to mitigate fraud risks and ensures the integrity of financial operations.

For the Analytics Developer role at Wealthsimple, you should have strong SQL skills with at least one year of applied experience. Familiarity with Python, data visualization tools like Tableau or PowerBI, and a passion for data-driven solutions are preferred. Ideally, you should demonstrate the ability to tailor data solutions for both technical and non-technical stakeholders.

Wealthsimple fosters a remote-first work culture, with a team of over 1,000 employees thriving across North America. We encourage collaboration and create a supportive atmosphere that values team diversity. You'll be part of a culture that promotes innovation, inclusivity, and a strong focus on employee wellbeing.

Wealthsimple is committed to the growth of our team members. As an Analytics Developer, you'll have access to a yearly budget of $1,500 for wellness and professional development. Plus, you'll be encouraged to contribute back to the analytics practice by championing best practices and sharing your insights with the team.

The benefits for an Analytics Developer at Wealthsimple include competitive salary packages, comprehensive health benefits, a retirement savings matching plan, and generous vacation policies. Plus, you can benefit from wellness days and a unique 90-day away program that allows you to work internationally!

Analytics Developers play a crucial role in combating financial crime at Wealthsimple. You will develop machine learning models and automated rules that prevent fraud and illegal activities on our platform, directly impacting our ability to protect clients and mitigate significant financial losses.

As an Analytics Developer, you'll work on projects that involve transforming vast amounts of client data into insightful analytics. You'll collaborate with Fraud and AML teams to build dashboards, develop metrics, and create automated reporting that drives strategic decision-making in our fight against financial crime.

During the interview, emphasize your practical experience with SQL and specific projects you've worked on that involved data analysis. Showcase your ability to write complex queries and the impact your analysis had on your previous teams.

Discuss your strategies for maintaining data integrity, such as implementing data validation rules, using version control systems, or conducting regular audits on data sets. Providing examples of past experiences can illustrate your commitment to accuracy.

Share a specific instance where you worked with stakeholders from different departments. Highlight the challenge, how you facilitated communication, and the outcome. This shows your collaborative skills and ability to navigate complex team dynamics.

Explain your experience with data visualization tools, such as Tableau or Looker. Describe a project where you transformed raw data into a visual format that influenced decision-making, emphasizing how effective visualization can simplify complex information.

Talk about your familiarity with automation tools or scripting languages, like Python or dbt, to streamline reporting processes. Explain the benefits of automation in reducing manual workload and improving efficiency.

Discuss your commitment to continuous learning, whether through online courses, networking, or hands-on practice. Share examples of technology or methodologies you’ve recently adopted and how they improved your work.

Showcase your analytical skills by discussing how you would identify key metrics that gauge both fraud risk and client satisfaction. Talk about strategies that could strike a balance, such as using data to reduce false positives while maintaining security.

Provide a concrete example of how your data analysis led to a significant decision in your team or organization. Highlight the data you used, how you presented it, and the impact it had.

Share any relevant experience with developing or utilizing machine learning models in your past roles. Elaborate on the project, your specific contributions, and the implications of the results.

This question gives you a chance to express your enthusiasm for the role and the company. Mention Wealthsimple's mission to provide transparent financial services and how your skills in data analysis will contribute to this mission while aligning with your career interests.