JPMorganChase
Applied AIML Data Scientist - Senior Associate
JPMorganChase, Jersey City, New Jersey, United States, 07390
JOB DESCRIPTION
The Risk Management & Compliance Technology Machine Learning team at JPMorgan Chase focuses on solving challenging business problems such as Anti-Money Laundering and Surveillance through data science and machine learning techniques across Risk, Compliance, Conduct and Operational Risk. As an Applied AI ML Senior Associate on the team, you will have the opportunity to study complex business problems and apply advanced algorithms to develop, test, and evaluate AI/ML applications or models for those problems.
You will work with the firm’s rich data pool from both internal and external sources using Python/Spark via AWS and other systems. You are also expected to derive business insights from technical results and be able to present them to a non-technical audience.
Job responsibilities
Proactively develop understanding of key business problems and processes.
Execute tasks throughout a model development process including data wrangling/analysis, model training, testing, and selection.
Generate structured and meaningful insights from data analysis and modeling exercises and present them in appropriate format according to the audience.
Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions.
Carry out ad-hoc and periodic analysis as required by the business stakeholder, model risk function, and other groups.
Required qualifications, capabilities, and skills
2-3 years of relevant experience post Advanced degree (MS or PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, and Econometrics).
Practical expertise and work experience with ML projects, both supervised and unsupervised.
Proficient programming skills with Python, R, or other equivalent languages.
Demonstrated experience working with large and complicated datasets.
Experience with a broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, and XGBoost.
Excellent problem-solving, communication (verbal and written), and teamwork skills.
Preferred qualifications, capabilities, and skills
Experience with graph analytics and neural networks (Tensorflow, Keras).
Experience working with engineering teams to operationalize machine learning models.
Familiarity with the financial services industry.
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The Risk Management & Compliance Technology Machine Learning team at JPMorgan Chase focuses on solving challenging business problems such as Anti-Money Laundering and Surveillance through data science and machine learning techniques across Risk, Compliance, Conduct and Operational Risk. As an Applied AI ML Senior Associate on the team, you will have the opportunity to study complex business problems and apply advanced algorithms to develop, test, and evaluate AI/ML applications or models for those problems.
You will work with the firm’s rich data pool from both internal and external sources using Python/Spark via AWS and other systems. You are also expected to derive business insights from technical results and be able to present them to a non-technical audience.
Job responsibilities
Proactively develop understanding of key business problems and processes.
Execute tasks throughout a model development process including data wrangling/analysis, model training, testing, and selection.
Generate structured and meaningful insights from data analysis and modeling exercises and present them in appropriate format according to the audience.
Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions.
Carry out ad-hoc and periodic analysis as required by the business stakeholder, model risk function, and other groups.
Required qualifications, capabilities, and skills
2-3 years of relevant experience post Advanced degree (MS or PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, and Econometrics).
Practical expertise and work experience with ML projects, both supervised and unsupervised.
Proficient programming skills with Python, R, or other equivalent languages.
Demonstrated experience working with large and complicated datasets.
Experience with a broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, and XGBoost.
Excellent problem-solving, communication (verbal and written), and teamwork skills.
Preferred qualifications, capabilities, and skills
Experience with graph analytics and neural networks (Tensorflow, Keras).
Experience working with engineering teams to operationalize machine learning models.
Familiarity with the financial services industry.
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