Applied AIML Data Scientist - Associate
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About this role
The Legal Applied AI/ML team in Corporate Technology at JPMorgan Chase focuses on solving challenging business problems such as semantic search, question answering, document analysis, automation of service inquiries through data science and ML techniques, particularly using GenAI and LLM tools and techniques. As an Applied AI ML 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 GenAI tools and frameworks, 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 non-technical audience.
Job responsibilities
- Proactively develop understanding of key business problems and processes
- Develop GenAI and LLM solutions to solve business problems
- 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 modelling exercise and present them in appropriate format according to the audience.
- Collaborate with other data scientists and ML engineers to deployment ML 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
- Advanced degree (MS) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, and Econometrics)
- Practical expertise with LLM projects as well as other supervised and unsupervised techniques.
- Proficient programming skills with Python, R, or other equivalent languages,
- Demonstrated experience working with large and complicated datasets.
- Experience with 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 network (Tensorflow, Keras).
- Experience working with engineering teams to operationalize ML models.
- Familiarity with the financial services industry.