KeyCorp

QuantitativeAnalyticsManager-ModelRisk

$116–216k Cleveland, Ohio, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Manager candidates.

The Brief

“Quantitative Analytics Manager- Model Risk at KeyCorp. Skills: Model Risk, Quantitative Analytics, Machine Learning, Data Validation. Lead validation of predictive models. Lead validation of machine-learning models”

What You'll Achieve.

Ensuring reliable answers to 'What will happen and how confident are we in the results'; Driving efficiency in solution delivery

Industry & Context.

Problems you'll solve

Provide solutions to business problems; Critical thinker; Problem-solving

What They're Looking For.

Must Have

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, 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, Understanding of Pros / Cons of competing analysis methods, Experience leading by partnering with others to anticipate and understand needs process/procedures, Experience leading information practices / policies / procedures, Experience setting standards and expectations for data analysis tools and ensuring compliance with application, Experience promoting increased efficiency of data analysis by advocating clearer data requirements, Advanced modeling techniques, Machine learning methods, 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, Efficient coding, Can build 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, Ability to establish standards and best forecast future modeling tools / techniques, Ability to identify, employ, and evangelize emerging techniques from industry / research, Ability to coach others on data modeling methods / techniques, Ability to facilitate sessions for complex data models, Ability to assess and understand contingency plans, Ability to communicate observations to senior executives, Ability to translate technical observations to a non-technical audience

Nice to Have

PhD preferred, specific ML framework experience, cloud platform certs

What You'll Do.

Lead validation of predictive models

Lead validation of machine-learning models

Use statistics and advanced techniques

Provide solutions to predictive questions

Provide solutions to prescriptive questions

Guide team through business problems

Assess and validate models

Assess and validate inferential methodologies

Assess and validate analytical frameworks

Evaluate model appropriateness

Evaluate model robustness

Evaluate model effectiveness

Ensure reliable answers

Identify and articulate observations

Evaluate impact on model soundness

Evaluate impact on model reliability

Evaluate impact on business use

Proactively coach others

Assess and challenge data preparation practices

Engage with data stewards

Review data traceability

Review data efficiency

Evaluate appropriateness of analytical methods

Assess suitability of methods

Assess justification of methods

How You'll Work.

Team & Collaboration

Partnering with others to anticipate and understand needs; Coaching others on developing technical skills; Engage and partner at mid to senior leadership; Develop relationship building skills in others; Manages the integration of activities; Work with product team

Communication Scope

Demonstrating leadership through communication skills; Presenting holistic, thoughtful analyses; Communicating technical theories, observations, and models to a non-technical audience; Excellent writing skills; Develops writing skills; Deliver the right message; Articulates broad implications / impact; Addresses challenging questions; Presents with executive presence; Comfortable presenting to senior levels

Process & Methodology

Managing competing priorities, Prioritizing multiple projects, Managing to tight deadlines

Full Job Description

**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 Quantitative Analytics Manager 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 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. Success factors include: 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 **ESSENTIAL JOB FUNCTIONS** * Independently assess and validate models, inferential methodologies, and analytical frameworks to evaluate their appropriateness, robustness, and effectiveness in addressing business needs and ensuring reliable answers to “What will happen and how confident are we in the results”, including CECL, Stress Testing, and Consumer/Commercial Credit Risk models * Often responsible for large, complex problems that have broad implications and are less frequent * Identify and articulate observations based on a structured asses

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