Quantitative Analytics Lead Associate

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

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

Selecting and retrieving data including unstructured data retrieval, archival, and ETL

Databases

Create data structures / transformations

SQL/NoSQL

Relationship data structure

Efficient coding

Can build strong code controls and translate code into high-level commentary

Distributed computing

MODEL BUILDING & MAINTENANCE

Cloud-based computing

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

Model use, requirements, and implementation needs

Effectively explain model insights to peers and analytics community

Identify preferred approach given the problem statement

Produce and identify information through statistical analysis

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

Key has implemented an approach to employee workspaces which prioritizes in-office presence, while providing flexible options in circumstances where roles can be performed effectively in a mobile environment.