Senior Manager, Data Scientist, Card Fraud Prevention

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: McLean, VANew York, NY
Salary: $229,900–$262,400 / yr
Employment: Full-time
Posted: 77 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $193k

This role $246k

$150k $274k

below market most similar roles pay here above market

This role pays more than 85% of similar roles. Most pay $162,000–$223,800 — the blue 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 2107 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1107 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Senior Manager, Data Scientist, Card Fraud Prevention

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The Senior Manager, Data Scientist - Card Fraud Prevention joins the Card Fraud Prevention data science team to detect and mitigate fraud by building and deploying machine learning models. This role involves partnering with cross-functional teams of data scientists, software engineers, and product managers to deliver production-ready insights. You will build models through all phases of development, including design, training, evaluation, validation, and implementation. The position requires leveraging a modern tech stack including Python, Spark, Ray, H2O, PyTorch, Kubernetes, Conda, and AWS to analyze huge volumes of numeric and textual data. Key technical competencies include experience with relational databases, clustering, classification, sentiment analysis, time series, and deep learning. The work focuses on keeping customer accounts safe and compliant by solving complex fraud detection problems at scale.

Skills

Python Spark Ray H2O PyTorch Kubernetes AWS Conda Scala R Machine Learning Deep Learning Time Series Sentiment Analysis Clustering Classification Relational Databases

What you'll do

Build and deploy machine learning models to detect and mitigate credit card fraud.
Develop production-ready insights using Python, Spark, H2O, PyTorch, and Kubernetes.
Manage the full machine learning lifecycle from design and training to evaluation and implementation.
Analyze and combine large volumes of numeric and textual data from diverse sources.
Translate complex technical findings into tangible business goals for stakeholders.
Research and evaluate emerging technologies and state-of-the-art methods for application.
Identify and improve the status quo by challenging conventional thinking.

What we're looking for

Must have a Bachelor's in a quantitative field plus 7 years of data analytics experience, a Master's plus 5 years, or a PhD plus 2 years.
Must have at least 2 years of experience leveraging open source programming languages for large scale data analysis.
Must have at least 2 years of experience working with machine learning.
Must have at least 2 years of experience utilizing relational databases.
Must have experience with clustering, classification, sentiment analysis, time series, and deep learning.
Must have experience developing data science solutions using open-source tools and cloud computing platforms.
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 managing people (preferred).
At least 5 years of experience in Python, Scala, or R for large scale data analysis (preferred).
At least 5 years of experience with machine learning (preferred).

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