Quantitative Analytics Manager- Model Risk
$116,000–$216,000 year
Remote · United States or Cleveland, Ohio, United States
Job Summary
Lead and validate quantitative models across business lines, guiding a team to address predictive and prescriptive questions, with strong emphasis on model soundness, data quality, and stakeholder communication. Responsibilities include assessing inference methodologies, reviewing deliverables, coaching others on analytical approaches, and ensuring robust results for CECL, stress testing, and credit risk models. Required skills include advanced modeling (ML techniques), SQL/NoSQL, Python/R/SAS, data governance, leadership, and the ability to translate complex analyses into strategic insights for senior partners. The role emphasizes in-office presence with flexible options and offers a base salary range of $116k-$216k plus incentive compensation. The position is based in Cleveland, OH with remote-work flexibility within the United States.
Required Qualifications
- 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
- Strong knowledge of advanced modeling techniques including machine learning methods (e.g., XGBoost, LightGBM, Random Forest)
- Experience in model validation, risk assessment, data quality, data lineage, and governance
- Experience communicating technical observations to senior executives and non-technical audiences
- Leadership and coaching abilities; ability to guide teams and senior stakeholders
- Proficiency with SQL/NoSQL, Python/R/SAS; data retrieval, ETL, and data management
- Experience with CECL, stress testing, and consumer/commercial credit risk models
- Strong written and verbal communication, problem-solving, and project management skills
- Capability to establish standards and best practices for modeling and analysis
- Ability to manage multiple projects with tight deadlines
- Experience with cloud and distributed computing environments
- Experience in presenting to mid-to-senior leadership
- Understanding of analytics lifecycle and data-driven decision making
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