Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.
As a Applied AI ML Senior Associate at JPMorgan Chase within the Asset and Wealth Management , you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm s business objectives.
Job responsibilities
- Supports and develops an understanding of key business problems and processes.
- Advises a model development process, execute tasks including data wrangling/analysis, model training, testing, and selection. Strategically, implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation.
- Updates logically and conducts evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implements monitoring mechanisms to track model performance and ensure model reliability.
- Frequently communicates AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. From data analysis and modeling exercises, generate structured and meaningful insights and present them in an appropriate format according to the audience. Collaboratively, work with other data scientists and machine learning engineers to deploy machine learning solutions. As required by the business stakeholder, model risk function, and other groups, carry out ad-hoc and periodic analysis.
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification in applied AI/ML concepts and 3+ years applied experience
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API.
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
- Experience in building AI/ML models on structured and unstructured data along with model explainability and model monitoring.
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs.
- Experience with a broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, and XGBoost.
- Experience with graph analytics and neural networks (PyTorch).
- Excellent problem-solving, communication (verbal and written), and teamwork skills.
Preferred qualifications, capabilities, and skills
- Expertise in building AI/ML models on structured and unstructured data along with model explainability and model monitoring.
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
- Familiarity with the financial services industry.
Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.
As a Applied AI ML Senior Associate at JPMorgan Chase within the Asset and Wealth Management , you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm s business objectives.
Job responsibilities
- Supports and develops an understanding of key business problems and processes.
- Advises a model development process, execute tasks including data wrangling/analysis, model training, testing, and selection. Strategically, implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation.
- Updates logically and conducts evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implements monitoring mechanisms to track model performance and ensure model reliability.
- Frequently communicates AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. From data analysis and modeling exercises, generate structured and meaningful insights and present them in an appropriate format according to the audience. Collaboratively, work with other data scientists and machine learning engineers to deploy machine learning solutions. As required by the business stakeholder, model risk function, and other groups, carry out ad-hoc and periodic analysis.
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification in applied AI/ML concepts and 3+ years applied experience
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API.
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
- Experience in building AI/ML models on structured and unstructured data along with model explainability and model monitoring.
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs.
- Experience with a broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, and XGBoost.
- Experience with graph analytics and neural networks (PyTorch).
- Excellent problem-solving, communication (verbal and written), and teamwork skills.
Preferred qualifications, capabilities, and skills
- Expertise in building AI/ML models on structured and unstructured data along with model explainability and model monitoring.
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
- Familiarity with the financial services industry.
