This role pays less than
68%
of similar roles. Most pay
$180,987–$252,412
— the shaded band above.
At the midpoint, this role pays about
$192k
versus about
$217k
for comparable roles.
Based on 240 similar postings.
Employer
About Capital One Financial
Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking
Capital One Financial currently has
1783 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 942 roles with salary data.
Manager Data Science - Global Payment Network AI Foundations joins the Global Network Analytics team within the Foundational AI & Automation pillar. In this role, you will partner with cross-functional teams of data scientists, software engineers, and product managers to build machine learning engines and AI agents. You will develop core models to power merchant intelligence, acceptance growth, fraud decisioning, and enterprise automation by evaluating billions of network transactions. The position requires expertise in Natural Language Processing and Large Language Models to extract insights from numerical, tabular, and textual data. You will utilize a modern tech stack including PyTorch, AWS, VectorDBs, and LLMs. Key responsibilities involve fine-tuning models, translating technical complexity into business value, and delivering scalable solutions for the payments ecosystem.
What does a Data Science Manager earn?
Median $211200 from 33 postings across 11 companies.
Deliver AI-powered capabilities to evaluate billions of network transactions and transform merchant intelligence.
Leverage PyTorch, AWS, VectorDBs, and LLMs to extract insights from complex numerical, tabular, and textual data.
Serve as the domain expert for adapting and fine-tuning Natural Language Processing (NLP) and Large Language Models.
Translate complex technical machine learning concepts into tangible business value and strategic goals.
Design and implement AI workflows and autonomous AI agents for scalable intelligence across the payments ecosystem.
Develop and deliver production-level libraries, platforms, or solution code for existing products.
Train language models using techniques such as training optimization, self-supervised learning, explainability, and RLHF.
What we're looking for
Must have a Bachelor's in a quantitative field plus 6 years of data analytics experience, a Master's plus 4 years, or a PhD plus 1 year.
Must have at least 1 year of experience leveraging open source programming languages for large scale data analysis.
Must have at least 1 year of experience working with machine learning.
Must have at least 1 year of experience utilizing relational databases.
Must have hands-on experience working with LLMs and solutions using open-source tools and cloud computing platforms.
Must have experience in training language models and expertise in subdomains like training optimization, self-supervised learning, explainability, or RLHF.
Must have a track record of delivering models at scale and delivering libraries, platforms, or solution-level code to existing products.
Preferred: PhD in a STEM field plus 3 years of experience in model development.
Preferred: At least 4 years of experience in Python, Scala, or R.
Preferred: At least 4 years of experience with machine learning and SQL.
Preferred: Experience with LLMs, including embedding models and vector databases.
Preferred: Experience designing and implementing AI workflows and autonomous AI agents.
Python
AWS
H2O
Spark
Conda
Machine Learning
Relational Databases
Scala
R
Deep Learning
Sentiment Analysis
Time Series
Clustering
Classification
Big Data
Data Analytics
Python
SQL
Pytorch
AWS
Hugging Face
LangChain
Lightning
VectorDBs
Natural Language Processing
Large Language Models
Machine Learning
Relational Databases
Scala
R
RLHF
Self-supervised Learning