Quantitative Analytics Manager- Model Risk

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 assessment of context, interdependencies, and analytical outcomes, and evaluate their impact on model soundness, reliability, and business use Reviews deliverables; proactively coaches others on approach and work product
• Assess and challenge data preparation practices against established standards and model requirements, engage with data stewards to review data quality, traceability, and efficiency from a validation perspective
• Evaluate the appropriateness of analytical methods used and assess whether they are suitable and well‑justified for the given context

REQUIRED QUALIFICATIONS

• Master’s degree (or tis 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

DATA LITERACY

• 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

TECHNOLOGY & TECHNIQUES

• Advanced modeling techniques, including machine learning methods (e.g., XGBoost, LightGBM, Random Forest), with the ability to evaluate, challenge, and validate model design, tuning approaches, and performance testing
• 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 Validation & Review

• 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

EXPECTED COMPETENCIES

• Leadership: Demonstrated leadership; may have direct reports; Assumes accountability for their work; Sought out for advice; Proactively coaches and guides the work of others; Manages the integration of activities typically within own team; Demonstrates executive presence; Offers an opinion, contributes to the conversation
• Partnering / Influencing: Demonstrated ability to engage and partner at mid to senior leadership levels; Established reputation and track record as an effective and collaborative partner; Coaches and develops relationship building skills in others; Demonstrates managerial courage; willing to dissent from others; leverages organizational and professional savvy and persuasive skills to influence others
• Business Acumen: Understands LOB and KeyCorp strategy; Leverages knowledge of our competition and the business to anticipate needs and make recommendations; Understands how business works; Contributes materially to LOB strategy
• Critical Thinking / Problem Solving: Critical thinker: able to anticipate business partner needs; Sees the “bigger picture”; Advises leaders to make informed decisions based on keen critical thinking and problem-solving ability; Sought out for perspective and guidance with tackling challenges; Can make decisions; considers longer term business strategy in recommending solutions
• Communication: Excellent writing skills; develops writing skills in others; Recognizes the need to deliver the right message at the right tie through the right channel; Articulates the broad implications / impact of the message; Anticipates and addresses conflict; Addresses challenging situations; does not shy away from a tough conversation; Strong presentation development; can coach and guide others to get to the appropriate level of detail and send an effective message; Comfortable presenting to senior levels, easily adapts / changes course, presents with confidence; Demonstrates executive presence