Lead Quantitative Analytics Associate II - Business Banking & Consumer Analytics

KeyBank • Brooklyn, OH

1d • $71,000 - $125,000 • Hybrid

About The Position

Under some supervision, the Lead Quantitative Analytics Associate is primarily responsible for using statistics, advanced mathematical techniques, and/or computer science to develop and validate predictive and machine-learning models for specific business needs. The Lead Quantitative Analytics Associate leverages advanced mathematical knowledge and analysis to provide solutions to predictive and prescriptive questions such as “What will happen next?” and “What will we do?”. Often large in scope, projects undertaken by the Lead Quantitative Analytics Associate involve self-directed data analysis and model building in response to a problem statement proposed by a business partner. Success factors include: timely and effective completion of tasks assigned by manager with manager and/or peer guidance; exercising functional knowledge in analytical programming languages, data literacy, and model development; effective communication of insights and data to peers; and developing work autonomy and problem-solving.

Requirements

• Bachelor’s degree (or its equivalent) in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, or other quantitative disciplines and at least 2 years of relevant experience; 1 with Master’s or PhD
• Understanding of and ability to: Create data structures / transformations
• Identify and capture different types of information for business needs or necessary for analysis
• Data controls
• Hypothesis testing / root-cause analysis
• Leverage and anticipate considerations in implementation
• Advanced Microsoft Office Suite
• Traditional (e.g. Linear Regression, Logistic Regression) with the ability to design and optimize modeling evaluate, challenge, and validate model design, and performance testing
• Intermediate Python/SQL: Write and Read functions (Py) and Windows function (SQL)
• Understand Data Import and Joins
• Can build code controls and translate code into commentary
• Advanced modeling techniques, including machine learning methods (e.g., XGBoost, LightGBM, Random Forest), with the ability to design and optimize modeling evaluate, challenge, and validate model design, and performance testing
• Git
• Can build strong code controls
• Resolve Conflict
• Work Collaboratively contributing to one Codebase
• Advanced Python/SQL: Write and Read Class and Unite Test
• Write and Read Advanced Windows functions
• Can build strong code controls and translate code into high-level commentary
• Understanding of and ability to leverage: Cloud-based computing
• Distributed computing
• Agentic AI (LLM, MCP, RAG)
• Understanding of: Model use, requirements, and implementation needs
• Testing for deterioration and model health
• Fundamental concepts of Machine Learning
• How statistical measurements are used
• Ability to: Produce and identify information through statistical analysis
• Effectively explain model insights to peers and analytics community
• Identify preferred approach given the problem statement
• Understanding of: Model Risk Management process and foundations
• Scale concepts of Machine Learning
• Advanced data techniques for modeling frameworks
• Some self-direction, likely will need some guidance and supervision; Starting to anticipate possible business problems – improving something that already exists
• Developing relationship building and interpersonal skills; Partnerships and influence typically at peer or “working group” level; Building influencing skills; demonstrated in area of expertise or assigned LOB
• Understands business partner strategy and the business of banking at a high level; Asks the right questions; Understands upstream and downstream impacts
• Demonstrates critical thinking; Analyzes, identifies and recommends appropriate solutions to moderately complex problems; Can translate data and answer the “why” question; Starting to understand impacts / intersections with others
• Solid writing skills; Can cohesively present and organize information in support of findings and recommendations; Demonstrates confidence in communicating a message (typically narrow in scope); Can tell a compelling story with data and information; Emerging presentation development and delivery skills

Responsibilities

• Use traditional statistical methods and machine learning to develop, monitor, maintain, and implement models that address the right business need including CECL, Stress Testing, Account Management, Origination Scorecard and Macroeconomic Forecast
• Assess and challenge data preparation practices against established standards and model requirements, engage with data stewards to review data quality, traceability, and efficiency from a validation perspective
• Often responsible for large, complex problems that have broad implications and are less frequent
• Identify and articulate observations based on a structured assessment of context, interdependencies, and analytical outcomes, and evaluate their impact on model soundness, reliability, and business use
• Reviews deliverables; proactively coaches others on approach and work product
• Evaluate the appropriateness of analytical methods used and assess whether they are suitable and well justified for the given context

Benefits

• Eligibility for incentive compensation which may include production, commission, and/or discretionary incentives.
• Flexible options in circumstances where roles can be performed effectively in a mobile environment.
• Supportive teammates
• Flexible, inclusive work environment
• Challenging projects
• Accessible leaders
• Opportunities to grow in your position and your career

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level