Applied AI/ML [Multiple Positions Available]

JPMorgan Chase & Co.•Wilmington, DE

1d•Onsite

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 Model Risk Governance and Review (MRGR).

Requirements

Master's degree in Finance, Statistics, Computer Science, Mathematics, Engineering or related field of study plus 3 years of experience in the job offered or as Applied AI/ML, Data Scientist, Postdoctoral Researcher, Postdoc Research Associate, or related occupation.
PhD in Finance, Statistics, Computer Science, Mathematics, Engineering or related field of study plus 1 year of experience in the job offered or as Applied AI/ML, Data Scientist, Postdoctoral Researcher, Postdoc Research Associate, or related occupation.
Three (3) years of experience with Applying artificial intelligence (AI) and machine learning (AI/ML) techniques including Logistic Regression, Random Forest, and Gradient Boosting Machine to develop and implement classification and regression models on large-scale datasets.
Three (3) years of experience deploying data analysis, feature-engineering, and model development workflows using Python and SAS.
Any amount of experience utilizing Analytical and modeling methods including logistic regression, gradient boosting machine, multinomial regression, multivariate analysis, discriminant analysis, principal component analysis, factor analysis, and time series analysis.
Any amount of experience identifying quantitative relationship embedded within data to predict target variable and inform decision-making.
Any amount of experience utilizing Quantitative data management and analytics, including data extraction, cleaning, transformation, and visualization.
Any amount of experience designing and developing interactive Excel and PowerPoint reports with advanced functionalities, including LOOKUP, index match, data analysis add-ons, Pivots, and Visual Basic for Applications (VBA).
Any amount of experience performing data manipulation, structuring, design flow, and query optimization using programming languages including SQL and Python.
Any amount of experience processing large data sets using data containers, multithreading, and multiprocessing in PySpark.
Any amount of experience utilizing data science libraries including Pandas, Numpy, Scikit-learn, TensorFlow, Pytorch, Matplotlib and Seaborn.
Any amount of experience with software engineering fundamentals including version control using GitHub and Bitbucket.

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 Model Risk Governance and Review (MRGR).