Principal Associate, Data Scientist - LLM Customization Team

Capital One • McLean, VA

1d

About The Position

The AI Foundations LLM Customization team at Capital One is central to realizing the company's vision for LLMs and Generative AI. This role encompasses the entire research lifecycle, from initial research to building production systems, collaborating with product, technology, and business leaders to apply state-of-the-art AI to financial services. The successful candidate will drive experimentation, innovation, and the creation of next-generation experiences powered by emerging generative AI technologies. This involves partnering with a cross-functional team to deliver AI-powered products, leveraging a diverse technology stack including Pytorch, Hugging Face, AWS Ultraclusters, LangChain, and VectorDBs to extract insights from vast amounts of data. The role requires expertise in Natural Language Processing (NLP) to harness, adapt, and finetune Large Language Models (LLMs) for specific business applications and features. Responsibilities also include building NLP models through all development phases (design, training, evaluation, validation) and collaborating with engineering teams for scalable and resilient production system operationalization, as well as translating complex technical work into tangible business goals.

Requirements

• Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
• Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics
• Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)
• Comfortable with advanced ML and DL technologies including language models and passionate about developing further.
• Hands-on experience working with LLMs and solutions using open-source tools and cloud computing platforms.
• Experienced in training language models or large computer vision models.
• Expertise in one or more key subdomains such as: training optimization, self-supervised learning, explainability, RLHF.
• Engineering mindset as shown by a track record of delivering models at scale both in training data and inference volumes.
• Experience in delivering libraries, platforms, or solution level code to existing products.

Nice To Haves

• Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)
• At least 1 year of experience working with AWS
• At least 3 years’ experience in Python, Scala, or R
• At least 3 years’ experience with machine learning
• Experience with Large Language Models (LLM), Finetuning, and Deep Learning

Responsibilities

• Partner with a cross-functional team of data scientists, applied researchers, software engineers, machine learning engineers and product managers to deliver AI powered products that change how customers interact with their money.
• Leverage a broad stack of technologies — Pytorch, Hugging Face, AWS Ultraclusters, LangChain, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
• Be the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs), adapt and finetune them for business specific applications and features.
• Build NLP models through all phases of development, from design through training, evaluation, and validation; partnering with engineering teams to operationalize them in scalable and resilient production systems.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Number of Employees: 5,001-10,000 employees