This role pays more than
50%
of similar roles. Most pay
$156,450–$217,418
— the shaded band above.
At the midpoint, this role pays about
$173k
versus about
$187k
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
998 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 992 roles with salary data.
Principal Data Scientist, Customer Protection Agentic Claims Data Science joins the Bank Customer Protection Debit & Claims Data Science team to build machine learning models that help customers spend safely and resolve payment issues quickly. You will partner with cross-functional teams of data scientists, analysts, software engineers, and product managers to develop products by analyzing large volumes of numeric and textual data. The role involves building models through all phases of development, including design, training, evaluation, validation, and implementation. You will utilize a technical stack including Python, Conda, AWS, H2O, and Spark to identify signals in transaction activity and merchant patterns. Key methodologies include representation learning, gradient boosting machines, clustering, classification, sentiment analysis, time series, deep learning, and applying LLMs to solve real-world problems within the claims processing domain.
What does a Data Scientist earn in Virginia?
Median $145200 from 75 postings across 5 companies.
Build machine learning models for fraud detection and claims processing using gradient boosting and representation learning.
Analyze large volumes of numeric and textual data to identify signals of fraudulent activity or scams.
Manage the full machine learning lifecycle including design, training, evaluation, validation, and implementation.
Utilize a technical stack including Python, Spark, H2O, and AWS to extract insights from big data.
Translate complex technical findings into tangible business goals for non-technical stakeholders.
Develop models to improve the speed and efficiency of customer claims resolution systems.
Apply advanced techniques such as clustering, sentiment analysis, and deep learning to solve real-world problems.
What we're looking for
A Bachelor's degree in a quantitative field plus 5 years of experience performing data analytics.
A Master's degree in a quantitative field or an MBA with a quantitative concentration plus 3 years of experience performing data analytics.
A PhD in a quantitative field.
Experience building, validating, and backtesting machine learning models including clustering, classification, sentiment analysis, time series, and deep learning (preferred).
At least 3 years of experience with machine learning for predictive tasks (preferred).
At least 3 years of experience in Python (preferred).
At least 1 year of experience with SQL (preferred).
At least 1 year of experience working with AWS (preferred).
Python
AWS
Spark
SQL
Machine Learning
Deep Learning
Transformers
Graph ML
Agentic AI
Scala
R
Big Data
Clustering
Classification
Sentiment Analysis
Time Series
Python
AWS
Spark
SQL
Machine Learning
Deep Learning
Transformers
Graph ML
Agentic AI
Scala
R
Big Data
Clustering
Classification
Sentiment Analysis
Time Series
Statistical Modeling
Python
AWS
Machine Learning
Spark
SQL
H2O
Conda
Scala
R
Deep Learning
Sentiment Analysis
Time Series
Clustering
Classification
Big Data
Statistical Modeling
Python
AWS
Machine Learning
Spark
SQL
H2O
Conda
Scala
R
Deep Learning
Sentiment Analysis
Time Series
Clustering
Classification
Big Data
Statistical Modeling