Manager, Data Science - LLM Customization Team

Capital One•New York, NY

33d

About The Position

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. Team Description The LLM Customization team is on the cutting edge of GenAI and at the center of bringing our vision for AI at Capital One to life. The work of the AI Training Team touches every aspect of the model development life cycle and our deployed models in production drive business impact with visibility from our C-Suite. Our team creates unprecedented amounts of high quality data for training and testing GenAI models; we care about how it's created, what's in those datasets, and the impact they have We are invested in building capabilities for evaluating and monitoring generative models; these methods must be state of the art, easy to use, and trusted by our users and contributors Horizontal capabilities enable vertical use case work; the team builds search, summarization, RAG, and agentic workflows for integration in production applications across the company We learn from our colleagues, attend conferences, publish papers, and maintain strong connections to the research community.

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 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

Nice To Haves

PhD in "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

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 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 your interpersonal skills to translate the complexity of your work into tangible business goals.

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