Data Scientist Associate Senior

JPMorgan Chase · Banking · Bengaluru, Karnataka, India · Asset & Wealth Management

Associate Senior Data Scientist at JPMorgan Chase in Bengaluru, focusing on designing, deploying, and managing prompt-based LLM models for NLP tasks in financial services. The role involves prompt engineering, LLM orchestration, building data pipelines, and developing tools for model training and evaluation, with a requirement for Python, PyTorch/TensorFlow, cloud platforms, and MLOps.

What you'd actually do

Designs, deployment, and management of prompt-based models leveraging Large Language Models (LLMs) for diverse NLP tasks in financial services.
Drives research and application of prompt engineering techniques to enhance model performance, utilizing LLM orchestration and agentic AI libraries.
Collaborates with cross-functional teams to gather requirements and develop solutions that address organizational business needs.
Communicates complex technical concepts and results effectively to both technical and non-technical stakeholders.
Builds and maintain robust data pipelines and processing workflows for prompt engineering on LLMs, leveraging cloud services for scalability and efficiency.

Skills

Required

Formal training or certification on data science concepts and 3+ years applied experience
Proven experience in prompt design and implementation, or chatbot application development.
Strong programming skills in Python, with expertise in PyTorch or TensorFlow.
Experience building data pipelines for both structured and unstructured data.
Proficiency in developing APIs and integrating NLP or LLM models into software applications.
Hands-on experience with cloud platforms (AWS or Azure) for AI/ML deployment and data processing.
Excellent problem-solving skills and the ability to communicate ideas and results clearly to stakeholders and leadership.
Working knowledge of deployment processes, including experience with GIT and version control systems.
Familiarity with LLM orchestration and agentic AI libraries.
Practical experience with MLOps tools and practices to ensure seamless integration of machine learning models into production environments.

Nice to have

Familiarity with model fine-tuning techniques such as DPO (Direct Preference Optimization) and RLHF (Reinforcement Learning from Human Feedback).
Knowledge of Java and Spark.

What the JD emphasized

prompt-based models leveraging Large Language Models (LLMs)
prompt engineering
LLM orchestration and agentic AI libraries
prompt-based model training, evaluation, and optimization
Python
PyTorch or TensorFlow
cloud platforms (AWS or Azure)
LLM orchestration and agentic AI libraries
MLOps tools and practices

Other signals

LLM
prompt engineering
agentic AI
NLP
financial services

Read full job description

Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

As a Data Scientist Associate Senior at JPMorgan Chase within the Asset & Wealth Management, you will partner with various lines of business and functional teams to deliver innovative software solutions. You will have the opportunity to research, experiment, develop, and productionize high-quality machine learning models, services, and platforms that create significant business value. Additionally, you will design and implement scalable, reliable data processing pipelines and generate actionable insights to optimize business outcomes.

Job responsibilities

Designs, deployment, and management of prompt-based models leveraging Large Language Models (LLMs) for diverse NLP tasks in financial services.
Drives research and application of prompt engineering techniques to enhance model performance, utilizing LLM orchestration and agentic AI libraries.
Collaborates with cross-functional teams to gather requirements and develop solutions that address organizational business needs.
Communicates complex technical concepts and results effectively to both technical and non-technical stakeholders.
Builds and maintain robust data pipelines and processing workflows for prompt engineering on LLMs, leveraging cloud services for scalability and efficiency.
Develops and maintain tools and frameworks for prompt-based model training, evaluation, and optimization.
Analyzes and interpret data to assess model performance and identify opportunities for improvement.

Required qualifications, capabilities, and skills

Formal training or certification on data science concepts and 3+ years applied experience
Proven experience in prompt design and implementation, or chatbot application development.
Strong programming skills in Python, with expertise in PyTorch or TensorFlow.
Experience building data pipelines for both structured and unstructured data. Proficiency in developing APIs and integrating NLP or LLM models into software applications.
Hands-on experience with cloud platforms (AWS or Azure) for AI/ML deployment and data processing.
Excellent problem-solving skills and the ability to communicate ideas and results clearly to stakeholders and leadership.
Working knowledge of deployment processes, including experience with GIT and version control systems.
Familiarity with LLM orchestration and agentic AI libraries.
Practical experience with MLOps tools and practices to ensure seamless integration of machine learning models into production environments.

Preferred qualifications, capabilities, and skills

Familiarity with model fine-tuning techniques such as DPO (Direct Preference Optimization) and RLHF (Reinforcement Learning from Human Feedback).
Knowledge of Java and Spark.

Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

Job responsibilities

Designs, deployment, and management of prompt-based models leveraging Large Language Models (LLMs) for diverse NLP tasks in financial services.
Drives research and application of prompt engineering techniques to enhance model performance, utilizing LLM orchestration and agentic AI libraries.
Collaborates with cross-functional teams to gather requirements and develop solutions that address organizational business needs.
Communicates complex technical concepts and results effectively to both technical and non-technical stakeholders.
Builds and maintain robust data pipelines and processing workflows for prompt engineering on LLMs, leveraging cloud services for scalability and efficiency.
Develops and maintain tools and frameworks for prompt-based model training, evaluation, and optimization.
Analyzes and interpret data to assess model performance and identify opportunities for improvement.

Required qualifications, capabilities, and skills

Formal training or certification on data science concepts and 3+ years applied experience
Proven experience in prompt design and implementation, or chatbot application development.
Strong programming skills in Python, with expertise in PyTorch or TensorFlow.
Experience building data pipelines for both structured and unstructured data. Proficiency in developing APIs and integrating NLP or LLM models into software applications.
Hands-on experience with cloud platforms (AWS or Azure) for AI/ML deployment and data processing.
Excellent problem-solving skills and the ability to communicate ideas and results clearly to stakeholders and leadership.
Working knowledge of deployment processes, including experience with GIT and version control systems.
Familiarity with LLM orchestration and agentic AI libraries.
Practical experience with MLOps tools and practices to ensure seamless integration of machine learning models into production environments.

Preferred qualifications, capabilities, and skills

Familiarity with model fine-tuning techniques such as DPO (Direct Preference Optimization) and RLHF (Reinforcement Learning from Human Feedback).
Knowledge of Java and Spark.