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KeyBank · 3 weeks ago

Quant Analytics Lead Assoc - Fraud

United States

Full-time

Remote

Entry, Mid Level

$71K/yr - $122K/yr

2+ years exp

KeyBank is a national association that is seeking a Lead Quantitative Analytics Associate. The role involves using statistical and advanced mathematical techniques to develop and validate predictive and machine-learning models to address specific business needs, while conducting quantitative analysis and supporting business questions.

Banking

Responsibilities

Conduct quantitative analysis including hypothesis testing and root-cause analysis on large data sets with more autonomy

Support the working group by identifying types of information needed for analysis or to inform business questions create data structures/transformations to be leveraged by groups for analysis

Use statistical analysis and machine learning to develop, maintain, and anticipate considerations in implementation of models that address the right business need

Use critical thinking to use the right approach for each problem statement

Anticipate business need and make continuous improvements to models and processes

Qualification

Statistical analysisMachine learningPython/R/SASSQL/NoSQLData structuresModel Risk ManagementData literacyBusiness acumenCritical thinkingCommunicationLeadership

Required

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

Understanding of and ability to identify and capture different types of information for business needs or necessary for analysis

Understanding of and ability to implement data controls

Understanding of and ability to perform hypothesis testing / root-cause analysis

Advanced Microsoft Office Suite

SQL/NoSQL knowledge including relationship data structure, selecting and retrieving data including unstructured data retrieval, archival, and ETL, and databases

Advanced Python/R/SAS knowledge including databases, efficient coding, and ability to build strong code controls and translate code into high-level commentary

Understanding of and ability to leverage cloud-based computing

Understanding of and ability to leverage distributed computing

Understanding of model use, requirements, and implementation needs

Understanding of Model Risk Management process and foundations

Understanding of testing for deterioration and model health

Understanding of scale and fundamental concepts of Machine Learning

Understanding of how statistical measurements are used

Understanding of advanced data techniques for modeling frameworks

Ability to produce and identify information through statistical analysis

Ability to effectively explain model insights to peers and analytics community

Ability to identify preferred approach given the problem statement

Some self-direction, likely will need some guidance and supervision