Hybrid Lead Predictive Analytics & Data Science

KeyBank; Brooklyn (OH); Hybrid; USD 71,000 - 125,000; Full time

Job summary

KeyBank is seeking a Lead Quantitative Analytics Associate to develop and validate predictive and machine-learning models for business needs. The role requires strong statistical and programming skills and the ability to translate insights into actionable recommendations.

The position involves working with data science methods, hypothesis testing, and model health monitoring, with in-office presence prioritized but flexible hybrid options.

Qualifications

• Bachelor's degree (or equivalent) in a quantitative field such as statistics, mathematics, economics, financial engineering, data science, predictive modeling or related
• A minimum of 2 years of relevant experience or 1 year with a Master’s/PhD
• Experience with data structures, hypothesis testing, and model development is expected

Responsibilities

• Conduct quantitative analyses including hypothesis testing and root-cause analysis on large data sets with increasing autonomy
• Support the working group by identifying data needs and creating data structures/transformations for analysis
• Develop, maintain, and validate models using statistics and machine learning for business needs
• Communicate insights clearly to peers and stakeholders; advise on the best modeling approach
• Anticipate business needs and continuously improve models and processes

Skills

Leadership

Partnering / Influencing

Business Acumen

Critical Thinking / Problem Solving

Communication

Education

Bachelor's degree in statistics / mathematics / economics / financial engineering / data sciences / predictive modeling

Tools

SQL/NoSQL

Python/R/SAS

Cloud computing

Job description

KeyBank is seeking a Lead Quantitative Analytics Associate to develop and validate predictive and machine-learning models for business needs. The role requires strong statistical and programming skills and the ability to translate insights into actionable recommendations.

The position involves working with data science methods, hypothesis testing, and model health monitoring, with in-office presence prioritized but flexible hybrid options.