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$165,000–$227,337.
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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
2107 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 1107 roles with salary data.
The Principal Associate, Data Science - US Card Fraud Authentication Team joins a team operating at the intersection of fraud defenses and customer experience. In this role, you will identify opportunities within ambiguous fraud landscapes to design technical visions for authentication workflows and prototype innovative machine learning solutions. You will build models through all phases of development, including training, evaluation, and implementation, while partnering with cross-functional stakeholders to turn raw information into actionable fraud-prevention strategies. The position requires proficiency in Python, SQL, Spark, H2O, and AWS. You must possess experience with clustering, classification, sentiment analysis, time series, and deep learning. The work involves analyzing massive datasets to optimize authentication decisioning and stay ahead of evolving fraud patterns within a large-scale credit card environment.
Identify opportunities to use machine learning to improve fraud authentication workflows.
Design the technical vision and prototype innovative machine learning solutions.
Analyze massive datasets using Spark, AWS, and SQL to uncover hidden fraud patterns.
Build machine learning models through all phases of design, training, evaluation, and implementation.
Translate technical complexities into business strategies to influence stakeholders and achieve goals.
Develop actionable fraud-prevention strategies based on rigorous quantitative analysis.
What we're looking for
Must have a Bachelor's degree in a quantitative field plus 5 years of data analytics experience.
Must have a Master's degree in a quantitative field or an MBA with a quantitative concentration plus 3 years of data analytics experience.
Must have a PhD in a quantitative field.
Must have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
Must have experience with clustering, classification, sentiment analysis, time series, and deep learning.
Must know how to interpret a confusion matrix or a ROC curve.
Master’s degree in a STEM field plus 3 years of data analytics experience, or a PhD in a STEM field (preferred).
At least 1 year of AWS experience, 3 years of Python/Scala/R experience, 3 years of machine learning experience, and 3 years of SQL experience (preferred).
Python
AWS
Spark
H2O
SQL
Scala
R
Machine Learning
Deep Learning
Conda
Statistical Modeling
Sentiment Analysis
Time Series
Clustering
Classification
Data Analytics
Python
AWS
Spark
H2O
SQL
Scala
R
Conda
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistical Modeling
Big Data
Python
Spark
Ray
H2O
PyTorch
Kubernetes
AWS
Conda
Scala
R
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Relational Databases
Python
Snowflake
AWS
Spark
H2O
OneLake
SQL
Scala
R
Conda
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistical Modeling
Process Automation
Python
Snowflake
AWS
Spark
H2O
OneLake
SQL
Scala
R
Conda
Machine Learning
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Statistical Modeling
Process Automation