Lead Quantitative Analytics Associate

KeyBank

Lead quantitative analyst developing predictive and machine-learning models for KeyBank’s banking business. Performing statistical analysis, data engineering, and model maintenance for business decisions.

Posted 8/4/2026full-timeBuffalo • New York, Ohio • 🇺🇸 United StatesSenior💰 $71,000 - $125,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in quantitative analysis, including hypothesis testing and root-cause analysis, while effectively translating data insights for business applications. Proficient in statistical and machine-learning model development, with a strong understanding of data structures and transformations.

Highest-signal resume keywords

Quantitative AnalysisStatistical ModelingMachine LearningSQL/NoSQLPython, R, or SAS

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Hypothesis TestingRoot-Cause AnalysisData StructuresData TransformationsStatistical AnalysisModel Risk ManagementETLDatabase KnowledgeCoding EfficiencyModel Health Testing

Soft Skills

Solid Writing SkillsPresentation SkillsCollaboration

Tools & Technologies

Microsoft Office SuiteCloud-Based ComputingDistributed Computing

Industry Keywords

Banking OperationsBusiness Partner StrategyPredictive ModelingData Controls

Tech Stack

Tools & technologies

CloudETLNoSQLPythonSQL

About the role

Key responsibilities & impact

Conduct quantitative analysis, including hypothesis testing and root-cause analysis, on large datasets with increasing autonomy
Identify information needed for analysis and business questions
Create data structures and transformations for group analysis
Develop, maintain, and anticipate implementation considerations for statistical and machine-learning models
Ensure models address the appropriate business need
Select the appropriate analytical approach for each problem statement
Anticipate business needs and continuously improve models and processes
Translate data into insights and communicate findings to peers and the analytics community
Support working groups and collaborate with business partners

Requirements

What you’ll need

Bachelor’s degree (or equivalent) in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, or another quantitative discipline
Minimum of 2 years of relevant experience, or minimum of 1 year of experience with a Master’s or PhD
Understanding of data structures and transformations
Ability to identify and capture information needed for business needs or analysis
Knowledge of data controls, hypothesis testing, and root-cause analysis
Advanced Microsoft Office Suite skills
SQL/NoSQL knowledge
Knowledge of relational data structures
Experience selecting and retrieving structured and unstructured data, including archival and ETL
Database knowledge
Advanced Python, R, or SAS skills
Efficient coding and strong code controls
Ability to translate code into high-level commentary
Understanding of cloud-based and distributed computing
Understanding of model use, requirements, implementation needs, and Model Risk Management foundations
Ability to test for model deterioration and model health
Understanding of machine-learning fundamentals and statistical measurements used in modeling frameworks
Ability to produce and identify information through statistical analysis
Ability to explain model insights to peers and the analytics community
Ability to identify the preferred approach for a problem statement
Solid writing and presentation skills
Understanding of business partner strategy and banking operations
Ability to analyze and recommend solutions to moderately complex problems

Benefits

Comp & perks

Base salary of $71,000.00 - $125,000.00 annually
Eligibility for incentive compensation, which may include production, commission, and/or discretionary incentives
Flexible options for roles that can be performed effectively in a mobile environment
Flexible, inclusive work environment
Supportive teammates
Challenging projects
Accessible leaders
Opportunities to grow in the position and career
Reasonable accommodations for qualified individuals with disabilities or disabled veterans