Manager, Data Science, People Tech

About the Role

Data is at the center of everything we do at Capital One. As a Data Scientist at Capital One, you’ll be part of a team leading disruption at a whole new scale, using the latest in computing and machine learning technologies.

The People Tech team is a close-knit group focused on delivering exceptional experiences and tech innovations for Capital One's associates and business partners, providing modern engineering capabilities, services, and platforms.

Key Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers, and product managers to deliver AI powered products.
• Leverage a broad stack of technologies including Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs, and more to reveal insights within large volumes of numeric and textual data.
• Act as 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: design, training, evaluation, and validation.
• Partner with engineering teams to operationalize models in scalable and resilient production systems serving over 80 million customers.
• Translate the complexity of work into tangible business goals.

Requirements & Tech Stack

• Technologies: Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs
• Skills: Natural Language Processing (NLP), Large Language Models (LLMs), Machine Learning, NLP Model Development, Model Operationalization, Data Analysis, Relational Databases, Open Source Programming Languages.
• Experience:
• Taking models into production through the model risk compliance process.
• Working with LLMs and solutions using open-source tools and cloud computing platforms.
• Training language models or large computer vision models.
• Expertise in one or more key subdomains such as: training optimization, self-supervised learning, explainability, RLHF.
• Delivering models at scale (training data and inference volumes).
• Delivering libraries, platforms, or solution-level code to existing products.

Qualifications

• Basic Qualifications:

• 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.

• 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.

• 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).

• At least 4 years of machine learning experience.

• At least 4 years of AI modeling experience.

• At least 4 years of experience in Python, Scala, or R.

• At least 4 years of experience with SQL.

• Experience working with AWS.