Manager, Data Science, AI Foundations

About the Role

Data is at the center of everything we do at Capital One. The AI Foundations Specialist Models Data Science team builds and ships state-of-the-art scalable architecture and AI/ML solutions for Capital One’s award-winning mobile app. We partner with product, tech, and design teams to deliver app features that delight customers with dynamic and personalized experiences, enable them to chat with Capital One’s digital assistant Eno, or search for useful content. You will be the driving force to experiment, innovate, and create next-generation experiences powered by the latest emerging generative AI technologies.

Key Responsibilities

• Partner with a cross-functional team of data scientists, 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, AWS Ultraclusters, Hugging Face, LangChain, Lightning, 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 fine-tune them for customer-facing applications and features.
• Build machine learning and 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 that serve 80+ million customers.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Requirements & Tech Stack

• Pytorch
• AWS Ultraclusters
• Hugging Face
• LangChain
• Lightning
• VectorDBs
• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Generative AI technologies
• Machine Learning (ML)
• Deep Learning (DL)
• Open-source tools
• Cloud computing platforms

Qualifications

• Customer first: Passion for doing the right thing for customers.
• Innovative: Continually research and evaluate emerging technologies; stay current on published state-of-the-art methods, technologies, and applications.
• Creative: Thrive on bringing definition to big, undefined problems; enjoy asking questions and pushing hard to find answers; willing to share new ideas.
• Leader: Challenge conventional thinking; work with stakeholders to identify and improve the status quo; passionate about talent development.
• Technical: Comfortable with advanced ML and DL technologies including language models; passionate about further development; hands-on experience working with LLMs and solutions using open-source tools and cloud computing platforms.
• Influential: Passionate about AI/ML; ability to bring along a cross-functional team in breakthrough innovations; communicate clearly and effectively to share findings with non-technical audiences.
• 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.
• An engineering mindset demonstrated 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.

Basic Qualifications

• Currently have, or are in the process of obtaining a Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics.
• Currently have, or are in the process of obtaining 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 4 years of experience performing data analytics.
• Currently have, or are in the process of obtaining a PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics.
• At least 1 year of experience leveraging open-source programming languages for large-scale data analysis.
• At least 1 year of experience working with machine learning.
• At least 1 year of experience utilizing relational databases.

Preferred Qualifications

• PhD in a "STEM" field (Science, Technology, Engineering, or Mathematics).
• Experience working with AWS.
• At least 4 years’ experience in Python, Scala, or R.
• At least 4 years’ experience with machine learning.
• At least 4 years’ experience with SQL.