Senior Risk Modelling & Analytics Manager (Fintech)

The role

You will build and put into use quantitative risk models in Python, such as credit scorecards, probability of default, loss and fraud models. You will also help shape Jobber's risk strategy and coach two analysts in risk modelling.

What you will do

• Build and implement quantitative risk models end to end in Python, from raw data through validation to use in decisions
• Help develop Jobber's risk strategy, turning risk appetite into limits, rules and decision policies
• Design analytics-driven loss prevention systems, rules and limits for credit losses, chargebacks and fraud
• Help introduce expected credit loss measurement and other IFRS 9 concepts
• Analyze behavioural and transaction data to operationalize known fraud patterns and uncover new ones
• Partner with Risk Operations and Fintech leadership to translate their objectives into models, analysis and KPIs
• Evaluate third-party vendors and partner data that could improve models and decisions
• Coach analysts in risk concepts, modelling and decision support, and set the standard for documented work
• Work with the BI team on risk data, reports and dashboards, and with the Data Science team on ML and AI for risk models
• Use AI tools as a core part of daily work

Required qualifications

• Extensive experience in risk modelling or risk analytics at a fintech, bank, insurer or lender, with credit risk at the core
• A track record of building models, frameworks or programs that changed how decisions were made
• Hands-on quantitative model development, such as credit scorecards, PD/LGD/EAD, expected credit loss, stress testing, capital or fraud models
• Strong modelling and analysis skills and SQL to pull and shape data from complex relational structures
• A degree in a quantitative field such as mathematics, statistics, financial mathematics or a related discipline
• Deep analytical curiosity and the ability to test better ways of working
• Business acumen and strategic thinking, with clear data-backed recommendations
• Ability to explain a model and its limits to non-modellers and earn the confidence of senior stakeholders
• Enjoyment of coaching others and raising the bar for a team

Nice to have

• Fraud risk experience
• Experience with IFRS 9 or expected credit loss frameworks
• Understanding of capital markets, lending or payments, including chargebacks
• Experience using AI tools and agents to make your own work faster and better

Benefits

• Extended health benefits package with fully paid premiums for body and mind
• Matching in RRSP, TFSA or FHSA
• Stock options and equity rewards
• Annual stipends for health and wellness
• Dedicated Talent Development team with career coaching, learning and leadership programs

Before you apply

• Formal people management experience is not required
• Candidates must be based in Edmonton, Toronto, Vancouver or Kitchener-Waterloo

About the company

Jobber builds software that helps small home service businesses such as plumbers, painters and landscapers quote, schedule, invoice and collect payments. The company has been recognized by Great Place to Work and Deloitte Canada's Technology Fast 50 lists.