Quant Analytics Lead Assoc - Fraud

KeyBank-posted 1 day ago

$71,000 - $122,000/Yr

Full-time • Mid Level

Brooklyn, OH

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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.

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

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 SQL/NoSQL Relationship data structure Selecting and retrieving data including unstructured data retrieval, archival, and ETL Databases Advanced Python/R/SAS: Databases Efficient coding Can build strong code controls and translate code into high-level commentary Understanding of and ability to leverage: Cloud-based computing Distributed computing
MODEL BUILDING & MAINTENANCE Understanding of: Model use, requirements, and implementation needs Model Risk Management process and foundations Testing for deterioration and model health Scale and fundamental concepts of Machine Learning How statistical measurements are used Advanced data techniques for modeling frameworks 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
EXPECTED COMPETENCIES Leadership: Some self-direction, likely will need some guidance and supervision; Starting to anticipate possible business problems – improving something that already exists Partnering / Influencing: 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 Business Acumen: Understands business partner strategy and the business of banking at a high level; Asks the right questions; Understands upstream and downstream impacts Critical Thinking / Problem Solving: 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 Communication: 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

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