JPMorgan Associate Data Scientist - Generative AI 

India, Karnataka, Bengaluru 

93309136

Job Summary

As a data scientist in our team, you will have the opportunity to solve exciting business problems in the HR domain. You will be expected to apply your strong curiosity for data and your proven track record of successfully applying rigorous scientific methods with proficiency in ML Engineering and Generative AI.

Job responsibilities

• Build Artificial Intelligence (AI) solutions to automate HR Specialty Operations tasks aiming to increase operations efficiency, reduce risk, strengthen controls and increase productivity.
• Perform data analysis on employee data to support various product initiatives across HR specialty operations.
• Develop operational data quality rules for proactive operations data quality monitoring to ensure data accuracy, completeness, and consistency.
• Stay updated with the rapidly evolving space of Generative AI research and software engineering combining vast data assets with LLMs and Multimodal LLMs
• Use Generative AI to create and uplift standard operating procedures for various data operations teams to ensure consistency and best practices are followed.
• Collaborate with cross-functional teams to identify data-related issues and provide recommendations for improvement.
• Develop and maintain data documentation, including data dictionaries, data lineage, indexing and data flow diagrams.

Required qualifications, capabilities, and skills

• Masters in a quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Optimization, or Data Science.
• Comprehensive proficiency in data analysis using Python, SQL, R, Excel and experience with big data and scalable model training
• 4+ years of relevant experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas).
• Knowledge of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Langchain, OpenAI APIs, Vector databases, opensource Hugging Face Models)
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals.
• Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
• Curious, detail-orientedwith theability to work independently in a fast-paced environmentand motivated by complex analytical problems.

Preferred qualifications, capabilities, and skills

• Strong mathematical and statistical skills, including knowledge of exploratory data analysis, statistical models, GLM, decision trees, clustering, bootstrapping.
• Hands-on experience deploying and scaling ML and AI Models on Public cloud services like AWS, Azure, Google Cloud or IBM.
• Familiarity with developing Retrieval-Augmented Generation (RAG) solutions, advanced LLM prompts and Intelligent agents using the LLMs
• Demonstrable experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models.