This role pays more than
85%
of similar roles. Most pay
$162,000–$224,062
— the shaded band above.
At the midpoint, this role pays about
$246k
versus about
$193k
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
673 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 667 roles with salary data.
As a Senior Manager, Data Scientist - Bank Customer Protection Agentic Claims, you will join the Bank Customer Protection Debit & Claims Data Science team to build machine learning models that help customers resolve payment issues quickly. You will lead a small team of data scientists while remaining hands-on in data exploration, model development, and end-to-end solution deployment. Your work involves developing agentic workflows, representation learning, and gradient boosting machines to power real-time decision systems and identify fraud indicators. To succeed, you must navigate complex technical landscapes using LLMs, LangChain, Python, Ray, Spark, and AWS to process large volumes of numeric and textual data. You will also establish the datasets and evaluation protocols necessary for broader enterprise AI initiatives while managing cross-functional partnerships to improve claims management processes and reduce customer effort in resolving transaction disputes.
Develop machine learning models through all phases of development including design, training, evaluation, and implementation.
Build agentic workflows and use LLMs, LangChain, and Spark to process large volumes of numeric and textual data.
Lead a team of data scientists by removing ambiguity and shaping the technical roadmap for claims management.
Perform hands-on data exploration and own the end-to-end lifecycle of solution deployment.
Establish datasets, tools, and evaluation protocols to support broader enterprise AI initiatives.
Partner with cross-functional teams including engineers and product managers to deliver customer-facing products.
Identify and implement new techniques to improve claims resolution and fraud prevention indicators.
What we're looking for
A Bachelor's degree in a quantitative field plus 7 years of experience performing data analytics, or a Master's/MBA with 5 years, or a PhD with 2 years.
At least 2 years of experience leveraging open source programming languages for large scale data analysis.
At least 2 years of experience working with machine learning.
At least 2 years of experience utilizing relational databases.
A PhD in a STEM field plus 4 years of experience in data analytics (preferred).
At least 1 year of experience working with AWS (preferred).
At least 1 year of experience building agentic workflows (preferred).
At least 1 year of experience managing people, and at least 5 years of experience in Python and machine learning (preferred).
Python
AWS
SQL
Spark
H2O
Conda
Machine Learning
Deep Learning
Gradient Boosting
LLMs
Sentiment Analysis
Time Series
Clustering
Classification
Representation Learning
Big Data
Statistical Modeling
Python
Spark
Ray
H2O
PyTorch
Kubernetes
AWS
Conda
Scala
R
Machine Learning
Deep Learning
Relational Databases
Big Data
Time Series
Sentiment Analysis
Clustering
Classification
SQL
Python
R
SAS
Incrementality Testing
Data Visualization
Tableau
AI-assisted development tools
GitHub Copilot
Claude Code
Statistical Methods
Experimental Design
Marketing Analytics
Data Science