CCB Risk [Multiple Positions Available]
About The Position
Develop artificial intelligence and machine learning (AI/ML)-based solutions that drive key credit decisions in the risk management process. Work on strategic, highly visible machine learning projects and interact with business strategy, model governance, data management, and model implementation teams to drive business results. Apply analytical, data science, and machine learning skills to solve complex business problems. Work across teams to design, develop, and implement analytical models. Responsible for these models throughout the model's life cycle, including data cleanup, data analysis, model selection, ongoing performance monitoring, issue resolution, and model retirement. Monitor and validate existing models to support risk strategy and business functions. Liaise with business partners to ensure alignment of the modeling team's Book of Work with the strategic direction of the business. Contribute to the asks and requirements stemming from various regulatory exams. Prepare presentations and reports for both non-technical and technical audiences including senior management, risk strategy and MRGR.
Requirements
- Master's degree in Statistics, Computer Science, Mathematics, Engineering or related field of study plus 3 years of experience in the job offered or as CCB Risk, Risk Strategy Analytics Analyst, Information Scientist, or related occupation.
- PhD in Statistics, Computer Science, Mathematics, Engineering or related field of study plus 1 year of experience in the job offered or as CCB Risk, Risk Strategy Analytics Analyst, Information Scientist, or related occupation.
- One (1) year of experience with Credit risk management in the financial industry.
- One (1) year of experience performing machine learning quantitative analytics using Python and SAS.
- One (1) year of experience working with credit risk data using Snowflake, Oracle, SQL, Teradata, and Hive.
- Any amount of experience conducting credit risk analysis and working on entire account life cycle including acquisition, portfolio management, and collection.
- Any amount of experience performing business and personal financial statement analysis.
- Any amount of experience applying language models including GPT-4 and Claude and prompt engineering techniques including designing effective prompts, few-shot learning, chain-of-thought prompting, and retrieval-augmented generation to automate and enhance various business processes.
- Any amount of experience using analytical and modeling methods including decision tree, random forest, logistic regression, multivariate regression, gradient boosting machine, multinomial regression, multivariate analysis, discriminant analysis, principal component analysis and factor analysis, and clustering analysis.
- Any amount of experience using distributed computing techniques to process data sets with data containers, multithreading, and multiprocessing in PySpark and Scala.
- Any amount of experience designing and developing interactive Excel and PowerPoint reports with advanced functionalities, including Pivots, VLOOKUP, VBA macros, index match, and data analysis add-ons.
- Any amount of experience using Data science libraries including Pandas, NumPy, scikit-learn, TensorFlow, PyTorch, Matplotlib and seaborn, and software engineering fundamentals including version control using GitHub or Bitbucket.
- Any amount of experience defining a risk target and features.
- Any amount of experience identifying a quantitative relationship embedded in data.
- Any amount of experience managing and analyzing quantitative data, including data extraction, cleaning, transformation, and visualization.
- Any amount of experience performing data manipulation, structuring, design flow, and query optimization using programming languages including SQL and Python.
Responsibilities
- Develop artificial intelligence and machine learning (AI/ML)-based solutions that drive key credit decisions in the risk management process.
- Work on strategic, highly visible machine learning projects and interact with business strategy, model governance, data management, and model implementation teams to drive business results.
- Apply analytical, data science, and machine learning skills to solve complex business problems.
- Work across teams to design, develop, and implement analytical models.
- Responsible for these models throughout the model's life cycle, including data cleanup, data analysis, model selection, ongoing performance monitoring, issue resolution, and model retirement.
- Monitor and validate existing models to support risk strategy and business functions.
- Liaise with business partners to ensure alignment of the modeling team's Book of Work with the strategic direction of the business.
- Contribute to the asks and requirements stemming from various regulatory exams.
- Prepare presentations and reports for both non-technical and technical audiences including senior management, risk strategy and MRGR.
Benefits
- Comprehensive health care coverage
- On-site health and wellness centers
- Retirement savings plan
- Backup childcare
- Tuition reimbursement
- Mental health support
- Financial coaching
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
