Quantitative Analytics Lead

About the Role

The ideal candidate will have a deep understanding of machine learning modeling, credit underwriting, credit risk management, and fraud detection, as well as experience working with large-scale datasets and script languages such as Python. The ability to work independently and collaboratively, with strong problem-solving and critical thinking skills, is essential.

As a Quantitative Analytics Lead, you will have the opportunity to work on complex problems, develop and implement innovative solutions, and contribute to the company's continued growth and success. You will be part of a dynamic team that values collaboration, transparency, and accountability.

What You Will Do

• Perform independent challenges of machine learning models used for credit underwriting, credit risk, and/or fraud detection through thorough validation and ongoing monitoring
• Identify model weaknesses and opportunities for improvement, and collaborate with model owners to remediate model validation findings
• Partner cross-functionally to implement and maintain the company's MRM framework, ensuring timely resolution of audit and regulatory requests
• Develop and maintain documentation of model risk management processes and procedures
• Stay up-to-date with industry trends and developments in machine learning and model risk management, and apply this knowledge to improve the company's MRM framework
• Collaborate with internal audit, internal controls, and compliance teams to ensure alignment and effective implementation of MRM processes
• Develop and maintain metrics and reports to measure model performance and risk, and present findings to senior management and other stakeholders
• Participate in the development and maintenance of the company's risk management framework, including the identification and assessment of risks

What We Are Looking For

• 4-6 years of professional experience in related technical areas, such as model development, model validation, data science, or a related field
• Deep and broad knowledge and experience in machine learning modeling, credit underwriting, credit risk management, and/or fraud detection and fraud risk management
• Proven ability to work with script languages (e.g., Python) and large-scale datasets (e.g., SQL)
• Extensive experience with machine learning platforms and frameworks (e.g., scikit-learn, pyspark) and cloud-based coding environments and databases
• BS, MS, or PhD in a quantitative field, such as mathematics, data science, computer science, or a related field
• Oriented toward detail, curious about data/models/algorithms, skilled at critical thinking and problem solving
• Extraordinary interpersonal and verbal/written communication skills, with the ability to collaborate effectively with cross-functional teams

Nice to Have

• Experience working in a fintech or financial services company, with a strong understanding of credit underwriting and credit risk management
• Knowledge of regulatory requirements and industry standards for model risk management, such as SR 11-7
• Experience with data visualization tools, such as Tableau or Power BI
• Certification in a related field, such as Certified Data Scientist or Certified Analytics Professional

Benefits and Perks

• Competitive base salary, with a range of
  45,000 - $205,000 per year
• Equity rewards, with an equity grade of 5
• 100% subsidized medical, dental, and vision coverage for you and your dependents
• Monthly stipends for health, wellness, and tech spending
• Flexible paid time off, with a minimum of 15 days per year
• Remote work arrangement, with the ability to work from anywhere in Canada
• Opportunities for professional development and growth, with a focus on innovation and collaboration

• Tip: Develop a strong understanding of machine learning platforms and cloud-based coding environments, and be prepared to provide examples of your experience working with these technologies.
• Tip: Review the company's website and social media channels to gain a deeper understanding of their products and services, and be prepared to ask informed questions during the interview process.
• Tip: Be prepared to discuss your experience working with large-scale datasets, and provide examples of how you have applied machine learning algorithms to drive business outcomes.
• Tip: Emphasize your ability to work collaboratively with cross-functional teams, and provide examples of how you have effectively communicated complex technical concepts to non-technical stakeholders.
• Tip: Be prepared to discuss your approach to model risk management, and provide examples of how you have identified and mitigated model risk in previous roles.
• Tip: Research the company's culture and values, and be prepared to discuss how your own values and work style align with those of the company.
• Tip: Be prepared to negotiate your salary, and have a clear understanding of your market value and the company's compensation practices.