CCB Risk Program [Multiple Positions Available]

JPMorgan Chase & Co.•Jersey City, NJ

1d•$175,000 - $260,000•Onsite

About The Position

Duties: Design and develop machine learning models to drive impactful decisions for the card business throughout the customer lifecycle, including acquisition, account management, transaction authorization, and collection. Research, develop, document, implement, maintain, and support tools and frameworks for AI/ML model explainability and fairness. Utilize cutting-edge machine learning approaches and construct sophisticated models, including deep learning architectures on big data platforms. Collaborate with cross-functional teams, such as Marketing, Risk, Technology, and Model Governance, to deliver innovative modeling solutions and ensure successful production deployment. Work closely with senior management to develop modeling solutions and effectively communicate model results and insights to senior leaders. Ensure compliance with internal and external model governance and regulatory requirements, and design model explainability methods for risk estimation models. Utilize technical proficiency in SAS, Python, or equivalent programming languages for data analysis, modeling tasks, and the development of credit risk scorecards. Perform data cleaning, feature engineering, selection, and hyper-parameter tuning for statistical and advanced machine learning techniques. QUALIFICATIONS: Minimum education and experience required: Master's degree in Data Science, Mathematics, Statistics, Econometrics, Computer Science, Engineering, or related field of study plus 3 years of experience in the job offered or as CCB Risk Program Associate, Data Scientist, Software Engineer, or related occupation. The employer will alternatively accept a PhD in Data Science, Mathematics, Statistics, Econometrics, Computer Science, Engineering, or related field of study plus 1 year of experience in the job offered or as CCB Risk Program Associate, Data Scientist, Software Engineer, or related occupation. Skills Required: This position requires one (1) year of experience with the following: Designing, implementing, and managing machine learning pipelines on Spark, Hadoop, and HDFS for large-scale data processing; Developing and managing scalable machine learning models using AWS; Storing and retrieving data with Teradata, Snowflake, or Hive; Manipulating and analyzing data in Python, using libraries including Pandas, NumPy, Scikit-learn, and PyTorch; Developing and applying statistical models and machine learning techniques, including XGBoost, GLM, Random Forest, Neural Networks, Clustering, and K-Nearest Neighbors; Applying SHAP for global and local explainability in machine learning models; Maintaining version control, conducting code reviews, implementing automated testing, and utilizing continuous integration continuous deployment methodologies. Job Location: 575 Washington Blvd, Jersey City, NJ 07310. We offer a competitive total rewards package including base salary determined based on the role, experience, skill set, and location. For those in eligible roles, discretionary incentive compensation which may be awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. In addition, please visit: https://careers.jpmorgan.com/us/en/about-us. Full-Time. Salary: $175,000 - $260,000 per year.

Requirements

Master's degree in Data Science, Mathematics, Statistics, Econometrics, Computer Science, Engineering, or related field of study plus 3 years of experience in the job offered or as CCB Risk Program Associate, Data Scientist, Software Engineer, or related occupation. The employer will alternatively accept a PhD in Data Science, Mathematics, Statistics, Econometrics, Computer Science, Engineering, or related field of study plus 1 year of experience in the job offered or as CCB Risk Program Associate, Data Scientist, Software Engineer, or related occupation.
Designing, implementing, and managing machine learning pipelines on Spark, Hadoop, and HDFS for large-scale data processing
Developing and managing scalable machine learning models using AWS
Storing and retrieving data with Teradata, Snowflake, or Hive
Manipulating and analyzing data in Python, using libraries including Pandas, NumPy, Scikit-learn, and PyTorch
Developing and applying statistical models and machine learning techniques, including XGBoost, GLM, Random Forest, Neural Networks, Clustering, and K-Nearest Neighbors
Applying SHAP for global and local explainability in machine learning models
Maintaining version control, conducting code reviews, implementing automated testing, and utilizing continuous integration continuous deployment methodologies.

Responsibilities

Design and develop machine learning models to drive impactful decisions for the card business throughout the customer lifecycle, including acquisition, account management, transaction authorization, and collection.
Research, develop, document, implement, maintain, and support tools and frameworks for AI/ML model explainability and fairness.
Utilize cutting-edge machine learning approaches and construct sophisticated models, including deep learning architectures on big data platforms.
Collaborate with cross-functional teams, such as Marketing, Risk, Technology, and Model Governance, to deliver innovative modeling solutions and ensure successful production deployment.
Work closely with senior management to develop modeling solutions and effectively communicate model results and insights to senior leaders.
Ensure compliance with internal and external model governance and regulatory requirements, and design model explainability methods for risk estimation models.
Utilize technical proficiency in SAS, Python, or equivalent programming languages for data analysis, modeling tasks, and the development of credit risk scorecards.
Perform data cleaning, feature engineering, selection, and hyper-parameter tuning for statistical and advanced machine learning techniques.