Data Scientist – Model Health & Governance

Job summary

The role focuses on monitoring the health, stability, and governance of production models within a Consumer Identity and Fraud environment. Key duties include analyzing large-scale datasets to investigate performance issues and maintaining model governance documentation.

Job details

Data Scientist – Model Health & Governance Contract: 6 months Location: Toronto or Markham, ON Start: ASAP Hours: 37.5 hours/week Positions: 1 Role Overview Seeking a Data Scientist to support the ongoing health, monitoring and governance of production models within a Consumer Identity and Access Management/Fraud environment. This role combines hands-on analytics with model monitoring, governance, documentation and stakeholder management. Key Responsibilities Monitor performance, stability, usage and data quality across a portfolio of production models. Use Python, SQL, Databricks and PySpark to analyze large-scale datasets and model performance. Investigate performance issues, threshold breaches, data anomalies and emerging risks. Perform trend analysis and quantitative assessments to support model governance decisions. Support model onboarding, annual reviews, validation, model changes and remediation activities. Prepare and maintain model governance documentation, monitoring plans and validation responses. Coordinate with Model Validation, Compliance, Risk, Technology, business stakeholders and model vendors. Review and challenge vendor model documentation and performance reporting. Develop monitoring reports, dashboards and executive-level presentations. Identify opportunities to automate and improve model monitoring and governance processes. Must-Have Requirements 2+ years of experience in Data Science, Analytics, Model Governance, Model Risk, Fraud Analytics, Financial Crime Analytics or a related field. Strong hands-on Python and SQL skills. Experience with Databricks and PySpark in large-scale data environments. Understanding of machine learning and AI methodologies, including: Supervised and unsupervised learning Classification and regression Ensemble methods Anomaly detection Emerging AI solutions Ability to analyze and monitor model performance and investigate data/model issues. Strong documentation, presentation and communication skills. Ability to explain technical model concepts to non-technical stakeholders. Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Risk Management or a related quantitative discipline. Nice to Have Experience working with Model Validation, Model Risk Management, Compliance, Audit or Regulatory teams. Experience reviewing third-party/vendor models. Banking or financial services experience. Fraud or financial crime model experience. Additional Details Potential for extension or conversion based on business needs and performance. Interview process consists of two stages, approximately one hour each. Note: We use AI tools to: obtain basic information, detect plagiarism, false employment history or references, categorize your skills, and do an initial match with job posting.

What you’ll do

The role focuses on monitoring the health, stability, and governance of production models within a Consumer Identity and Fraud environment. Key duties include analyzing large-scale datasets to investigate performance issues and maintaining model governance documentation.

Requirements

Candidates must have 2+ years of experience in data science or model risk with strong proficiency in Python, SQL, and PySpark. A Bachelor's or Master's degree in a quantitative field is required.

Listed skills

• Technical Documentation · Preferred
• SQL · Preferred
• Machine learning · Preferred
• Stakeholder Management · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• SQL
• Databricks
• PySpark
• Model Governance
• Model Monitoring
• Machine Learning
• Anomaly Detection
• Fraud Analytics
• Data Quality Analysis
• Stakeholder Management
• Model Validation
• Quantitative Assessment
• Trend Analysis
• Technical Documentation
• Financial Crime Analytics

Job areas

• Data & Analytics
• Finance & Accounting
• Technology
• Science & Research
• Consulting