Join JP Morgan Asset Management’s Data Science team to enhance investment processes using cutting-edge machine learning.
As a NLP/LLM Data Scientist Lead within the Asset Management Data & Analytics team, you will be at the forefront of enhancing and facilitating various steps in our investment process. You will apply your passion for data science and machine learning to generate actionable insights for our business partners. Your work will directly contribute to improving our investment process, enhancing client experiences, and optimizing operational processes. You will collaborate closely with business stakeholders, technologists, and control partners to deploy solutions into production. This role provides an exciting opportunity to make a real impact in the asset management industry, work with cutting-edge technologies, and continuously learn and experiment with the latest data science and machine learning techniques.
Job Responsibilities
Collaborate with internal stakeholders to identify business needs and develop NLP/ML solutions that address client needs and drive transformation. Apply large language models (LLMs), machine learning (ML) techniques, and statistical analysis to enhance informed decision-making and improve workflow efficiency, which can be utilized across investment functions, client services, and operational processes. Collect and curate datasets for model training and evaluation. Perform experiments using different model architectures and hyperparameters, determine appropriate objective functions and evaluation metrics, and run statistical analysis of results. Monitor and improve model performance through feedback and active learning. Collaborate with technology teams to deploy and scale the developed models in production. Deliver written, visual, and oral presentation of modeling results to business and technical stakeholders. Stay up-to-date with the latest research in LLM, ML, and data science. Identify and leverage emerging techniques to drive ongoing enhancement. Required Qualifications, Capabilities, and Skills
Advanced degree (MS or PhD) in a quantitative or technical discipline or significant practical experience in industry. Commercial experience in applying NLP, LLM, and ML techniques in solving high-impact business problems, such as semantic search, information extraction, question answering, summarization, personalization, classification, or forecasting. Advanced python programming skills with experience writing production quality code. Good understanding of the foundational principles and practical implementations of ML algorithms such as clustering, decision trees, gradient descent, etc. Hands-on experience with deep learning toolkits such as PyTorch, Transformers, HuggingFace. Strong knowledge of language models, prompt engineering, model finetuning, and domain adaptation. Familiarity with latest developments in deep learning frameworks. Ability to communicate complex concepts and results to both technical and business audiences. Preferred Qualifications, Capabilities, and Skills
Prior experience in an Asset Management line of business. Exposure to distributed model training and deployment. Familiarity with techniques for model explainability and self-validation. Company:
Chase- Candidate Experience page Level of Experience (Years):
Senior (5+ years of experience)
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Collaborate with internal stakeholders to identify business needs and develop NLP/ML solutions that address client needs and drive transformation. Apply large language models (LLMs), machine learning (ML) techniques, and statistical analysis to enhance informed decision-making and improve workflow efficiency, which can be utilized across investment functions, client services, and operational processes. Collect and curate datasets for model training and evaluation. Perform experiments using different model architectures and hyperparameters, determine appropriate objective functions and evaluation metrics, and run statistical analysis of results. Monitor and improve model performance through feedback and active learning. Collaborate with technology teams to deploy and scale the developed models in production. Deliver written, visual, and oral presentation of modeling results to business and technical stakeholders. Stay up-to-date with the latest research in LLM, ML, and data science. Identify and leverage emerging techniques to drive ongoing enhancement. Required Qualifications, Capabilities, and Skills
Advanced degree (MS or PhD) in a quantitative or technical discipline or significant practical experience in industry. Commercial experience in applying NLP, LLM, and ML techniques in solving high-impact business problems, such as semantic search, information extraction, question answering, summarization, personalization, classification, or forecasting. Advanced python programming skills with experience writing production quality code. Good understanding of the foundational principles and practical implementations of ML algorithms such as clustering, decision trees, gradient descent, etc. Hands-on experience with deep learning toolkits such as PyTorch, Transformers, HuggingFace. Strong knowledge of language models, prompt engineering, model finetuning, and domain adaptation. Familiarity with latest developments in deep learning frameworks. Ability to communicate complex concepts and results to both technical and business audiences. Preferred Qualifications, Capabilities, and Skills
Prior experience in an Asset Management line of business. Exposure to distributed model training and deployment. Familiarity with techniques for model explainability and self-validation. Company:
Chase- Candidate Experience page Level of Experience (Years):
Senior (5+ years of experience)
#J-18808-Ljbffr
