Intermediate Data Scientist x 2
Typical Day in Role
• Report directly to a Senior Manager or Director and be a critical member of a team overseeing risk parameter estimation models, and related internal and regulatory processes.
• You will support – from conception through execution and governance – the credit risk parameter estimation models covering the Bank’s AIRB Business Banking portfolio.
• You will collaborate, on a regular basis, with a wide range of stakeholders and internal partners including Model Validation and Governance, Finance, Business Lines Partners, Compliance and Audit.
• You will have access to a modern machine learning stack that includes open source development environments, and data visualization business intelligence tools.
• Under the guidance of your Director, your team of risk modeling experts will use these tools to develop advanced risk estimation models that will be used to make decisions worth $billions every month and therefore they need to be not only precise and accurate, but highly stable, explainable, compliant, secure and useful. You will be responsible for understanding the goals & priorities set for you, executing them efficiently with a perpetual eye on quality, asking questions often and delivering results in harmony with your teammates.
Sample projects that you might work on include:
• Develop, implement and maintain risk quantification methodologies for Business Banking credit risk parameters such as PD, LGD and EAD.
• Perform research and analysis of applicable methodologies; present and recommend appropriate alternatives; test and implement modelling methodologies.
• Benchmark internal results with external models or data sources; provide analysis and recommend actions as appropriate.
• Implement and maintain a rigorous framework of internal controls and comprehensive documentation for various applications and databases used in parameter estimation models.
• Communicate results of analyses through documentation to internal/external audiences, and effectively manage the interface with relevant parties such as Validation, Audit, and Regulators.
• Keep abreast with advances in credit risk analytics developments, products, and applications by vendors, consultants, regulatory agencies and competitors. Recommend/develop enhancements appropriate for the Bank.
Candidate Requirements/Must Have Skills:
• 3+ years’ experience in Python, SQL, and Git in a professional capacity. Excellent computing development skills, particularly statistical and database modeling tools, well-developed ability to adapt to various programming languages and environments.
• 3+ year of hands-on experience in quantitative analysis and machine learning; exposure to quantitative analysis related to credit risk management and modeling is preferred.
• In-depth understanding of statistical techniques and procedures related to analysis of various distributions, regression modeling, monte-carlo simulation and bootstrapping techniques.
• 1 + years of experience in hands-on quantitative/statistical analysis, preferably related to the non-retail credit risk area in a major financial institution.
• SAS, R, Access/VBA, etc. is an asset
• Experience developing credit risk models
• Experience working within Basel regulatory capital requirements framework
• Domain expertise with Business Banking exposures and/or risk management practices
• FRM, CFA credentials
• Experience training and deploying machine learning models using common Python open source frameworks (e.g., scikit-learn)
• Microsoft Office (Excel, Word, PowerPoint, Teams, PowerBI) power user
• Able to work remotely and on-site on multiple activities simultaneously and meet deadlines
• Ability to work as part of a team, as well as work independently or with minimal direction.
• Excellent written, presentation, and verbal communication skills.
• Ability to efficiently manage multiple priorities to ensure timely delivery
• Well-developed writing and presentation skills, including competence in comprehensively and concisely reporting and presenting the results of complex analyses.
• Ability to efficiently manage multiple priorities to ensure timely delivery.
• Attention to details, independence, and ability to effectively collaborate in teamwork.
• Flexibility and creativity in problem solving.
Degrees or certifications:
• A graduate degree (or equivalent) in Statistics, Computer Science, or comparable quantitative discipline that includes rigorous exposure to statistical knowledge and techniques.