Applied AI ML Associate

Applied AI ML Associate

Job Description

Join our Model Insights Team, a Center of Excellence within Consumer & Community Banking (CCB) Risk Modeling, committed to tracking of comprehensive health of machine learning models. We are responsible for sanity of model inputs and score performance tracking for CCB risk decision models. We are constantly seeking for opportunities to enhance model performance tracking framework, with aim of providing feedback loop to risk criteria team. We are seeking candidates with strong technical skills in building data-driven solutions, the ability to apply domain knowledge to deliver scalable, production-ready outcomes and a keen eye for detail and logic. It’s an opportunity to make an impact to model performance monitoring and governance practices for CCB risk models. 

 Job Responsibilities

• Partner with model development teams to understand model monitoring and reporting requirements and deliver viable end-to-end solutions, including data sourcing, ETL, transformations, and deployment.
• Engage stakeholders and build strong partnerships to identify and execute opportunities to enhance model performance monitoring and automation.
• Manipulate and analyze data to identify, troubleshoot, and resolve data quality and pipeline issues.
• Proactively research and evaluate diverse data sources, identify relevant variables and data points, and transform them into standardized metrics and attributes to support monitoring and reporting needs.
• Support BAU execution of model monitoring deliverables, including Leadership reporting, validation, and issue triage, in line with defined SLAs, while proactively identifying opportunities to improve efficiency through GenAI adoption and automation.

Required Qualifications, Capabilities, and Skills

• Experience in application design and development using SAS, Python, PySpark, and Snowflake, with strong ETL/database skills and hands-on exposure to system design, testing, and production support.
• Experience working on cloud computing platforms, preferably Databricks.
• Strong quantitative and analytical capabilities, with an ability to troubleshoot data/process issues and drive resolutions.
• Undergraduate degree (minimum) in Engineering, Physics, Mathematics, or another quantitative discipline.
• Strong communication and delivery skills: excellent written/verbal communication, proactive and team-oriented mindset, and solid organizational/project management skills.
• Strong risk and controls mindset, including the ability to ask the right questions, identify risks, escalate appropriately, 
• Prior experience in model performance/health monitoring and/or risk analytics (preferred).

 

Preferred Qualifications, Capabilities, and Skills

• Hands-on experience with Tableau dashboarding is a plus.
• Exposure to risk management within a financial services institution is an advantage.

Experience Level

Mid Level