Expert Risk Analyst

An Initial AI Screening will be conducted for this role. Duration: 12 months Daily Responsibilities: Responsible for managing, monitoring, and developing predictive risk models that support Small Business, Commercial Banking and Capital Markets in Commercial & Wholesale Credit Risk. Modelling responsibilities cover a wide variety of models including, but not limited to, Machine learning, Artificial Intelligence, credit scoring, and surveillance models for the purpose of reducing losses or driving revenue in the portfolio Responsible for sourcing data, engineering features, as well as developing, monitoring, and deploying models. Extract, clean, validate, and analyze usable data from multiple data sources/providers to quantify borrower behavioural patterns. Participate in data assessment and procurement for credit modelling and analytics, help automate the underlying credit modelling feature farm, assess and address data gaps, as well as develop, monitor, and deploy credit risk models. Engage with stakeholders and experts across adjudication and line-of-business throughout the model development cycle; solicit input from experts and ensure models are business-sound. Prepare model documentation, source code, presentation decks, and/or model monitoring reports. Responsible for resolving issues raised by independent validation, Internal Audit and ongoing model monitoring. What program/technology/software knowledge is essential for this role? Python, SQL and SAS Must-have Skills: Undergraduate degree in computer science, finance, mathematics, statistics, or economics, with at least 5 years of working experience in related credit risk modeling roles. Hands-on experiences with large datasets (ingestion, processing, merging and aggregation of data), with fluency in both SQL and big data/cloud technologies (Hadoop, PySpark, S3). Strong Python coding skills to support automation and efficient end-to-end model scoring/implementation. Strong understanding and working knowledge of advanced statistical methods and machine learning techniques for classification and regression tasks. Demonstrated knowledge of credit risk models and time series analysis. Experience in code sharing and version control solutions (GitHub). Ability to work with UNIX command line. Nice-to-have Skills: Master's degree in computer science, finance, mathematics, statistics, or economics. Knowledge of GenAI use cases in retail/commercial lending. Knowledge of other programming languages such as R, Java or SAS. Prior model development experience for IFRS9, stress testing or capital measurement. Soft skills: Ability to react to changing demands on an ad hoc basis Analytic/Systematic Thinking Problem Solving Conceptual Thinking Teamwork & Partnering Results Orientation Impact & Influence FP Inc. is committed to creating an inclusive environment where all team members and clients feel like they belong. In accordance with the requirements set out in the Employment Standards Act, FP Inc. hereby declares that AI is utilized in the screening process for this position. The hourly compensation range for this role is 60/hr -71/hr. We seek applicants with a wide range of abilities, and we provide an accessible candidate experience. We advocate for you and welcome anyone regardless of race, colour, religion, national origin, sex, physical or mental disability, or age.

Manage and develop predictive risk models using AI and machine learning to support commercial and wholesale credit risk. Responsibilities include sourcing data, engineering features, and collaborating with stakeholders to ensure models are business-sound.

Requires a bachelor's degree in a quantitative field and at least 5 years of experience in credit risk modeling. Proficiency in Python, SQL, and big data technologies like Hadoop and PySpark is essential.

• SQLPreferred
• GitHubPreferred
• Machine learningPreferred
• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Python
• SQL
• SAS
• Machine Learning
• Artificial Intelligence
• Credit Risk Modeling
• Hadoop
• PySpark
• S3
• GitHub
• UNIX
• Time Series Analysis
• Feature Engineering
• Data Validation
• Statistical Methods
• Regression Analysis

• Data & Analytics
• Finance & Accounting
• Technology
• Software
• Engineering

Minimum education

Bachelor’s degree

Minimum experience

5+ years

Apply by

Sep 24, 2026

Posting language

English

Working hours

40 hours per week

Seniority

Mid-Senior level