Model Risk - Quantitative Analytics Manager

Location

127 Public Square, Cleveland Ohio

The Quantitative Analytics Manager is primarily responsible for leading the validation of predictive and machine‑learning models for specific business needs using statistics, advanced mathematical techniques, and/or computer science. The role leverages advanced mathematical knowledge, analysis, partnerships, and business knowledge to provide solutions to predictive and prescriptive questions such as “What will happen next?” and “What will we do?” Projects undertaken by the Quantitative Analytics Manager are often broad in scope across multiple business segments and involve guiding a team and/or project through providing solutions to business problems leveraging statistics, best practices or emerging techniques, and quantitative tools/techniques.

Demonstrating leadership through strong communication skills, addressing conflict, coaching others on developing technical skills; managing competing priorities and presenting holistic, thoughtful analyses to answer partners’ problem statements; prioritizing multiple projects and managing to tight deadlines; establishing reputation as an effective and collaborative partner; communicating technical theories, observations, and models to a non‑technical audience; leveraging knowledge of strategy, business, and competition to connect day‑to‑day work of team to the “bigger picture” and driving efficiency in solution delivery.

Create and leverage models, inferential statistics, and prescriptive analysis to proactively solve business problems answering the questions “What will happen and what should we do about it?” Often responsible for large, complex problems that have broad implications and are less frequent. Recommend solutions based on understanding of the context, connections, and conclusions. Review deliverables; proactively coach others on approach and work product.

Lead and evangelize on best practices of capturing and retaining data. Coordinate with data stewards and anticipate needs, process/procedures. Make continuous improvements to data procedures, including data efficiency. Recommend best analysis method for the situation.

Master’s degree (or its equivalent) in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, or other quantitative disciplines and at least 5 years of relevant experience; or Bachelor’s degree (or its equivalent) in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, or other quantitative disciplines and at least 6 years of relevant experience.

Understanding of best practices for capturing/retaining data; pros/cons of competing analysis methods. Experience leading by partnering with others to anticipate and understand needs, process/procedures. Leading information practices/policies/procedures. Setting standards and expectations for data analysis tools and techniques; ensuring compliance with application. Promoting increased efficiency of data analysis by advocating clearer data requirements.

Advanced Microsoft Office Suite. SQL/No

SQL relational data structure. Selecting and retrieving data including unstructured data retrieval, archival, and ETL. Advanced Python/R/SAS: efficient coding. Ability to build strong code controls and translate code into high‑level commentary. Understanding of and ability to leverage cloud‑based computing and distributed computing.

Ability to establish standards and best practices; forecast future modeling tools/techniques. Identify, employ, and evangelize emerging techniques from industry/research. Coach others on data modeling methods/techniques. Facilitate sessions for complex data models. Assess and understand risks; contingency plans. Communicate observations to senior executives. Translate technical observations to a non‑technical audience.

Leadership:
Demonstrated leadership; may have direct reports; assumes accountability for their work; sought out for advice; proactively coaches and guides the work of others;…