Lead Quantitative Analytics Associate II - Business Banking & Consumer Analytics

## Lead Quantitative Analytics Associate II - Business Banking & Consumer Analytics Apply locations:
Brooklyn, OH:
Remote, United Statestime type:
Full time posted on:
Posted Todaytime left to apply:
End Date:
July 6, 2026 (30+ days left to apply) job requisition :
R-39963
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* Location:

** 4900 Tiedeman Road, Brooklyn Ohio## ABOUT THE JOB (JOB BRIEF)## 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.## ## ESSENTIAL JOB FUNCTIONS* ## 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## ## REQUIRED QUALIFICATIONS* ## 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## ## DATA LITERACY* ## 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## ## TECHNOLOGY & TECHNIQUES* ## 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, Light

GBM, 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)## ## MODEL BUILDING & MAINTENANCE* ##…