Manager, Data Science, AI Foundations

About the Role

The AI Foundations Specialist Models Data Science Team builds and ships state-of-the-art scalable architecture, 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 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 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 interpersonal skills to translate the complexity of work into tangible business goals.

Ideal Candidate Traits

• Customer first: Loves the process of analyzing and creating, shares passion to do the right thing for customers.
• Innovative: Continually researches and evaluates emerging technologies, stays current on published state-of-the-art methods, technologies, and applications, and seeks opportunities to apply them.
• Creative: Thrives on bringing definition to big, undefined problems, enjoys asking questions and pushing hard to find answers, and is not afraid to share new ideas.
• A leader: Challenges conventional thinking, works with stakeholders to identify and improve the status quo, and is passionate about talent development.
• Technical: Comfortable with advanced ML and DL technologies including language models, passionate about developing further, with hands-on experience working with LLMs and solutions using open-source tools and cloud computing platforms.
• Influential: Passionate about AI/ML and can bring along a cross-functional team in breakthrough innovations, communicates clearly and effectively to share findings with non-technical audiences.
• Experienced in training language models or large computer vision models, with expertise in one or more key subdomains such as: training optimization, self-supervised learning, explainability, RLHF.
• Possesses an engineering mindset as shown by a track record of delivering models at scale both in training data and inference volumes. Has experience in delivering libraries, platforms, or solution-level code to existing products.

Requirements & Tech Stack

• Pytorch
• AWS Ultraclusters
• Hugging Face
• LangChain
• Lightning
• VectorDBs
• Generative AI technologies
• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Machine Learning (ML)
• Deep Learning (DL)
• Reinforcement Learning from Human Feedback (RLHF)
• Open source programming languages
• Cloud computing platforms

Qualifications

• 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 6 years of experience performing data analytics.
• 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.
• 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 “STEM” field (Science, Technology, Engineering, or Mathematics).
• Experience working with AWS.
• At least 4 years of experience in Python, Scala, or R.
• At least 4 years of experience with machine learning.
• At least 4 years of experience with SQL.