Manager, Data Scientist, US Card

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type
On-site
Location
McLean, VANew York, NYCambridge, MA
Salary
$197,300–$225,100 / yr
Employment
Full-time
Posted
48 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 $180k
This role $211k
$131k $235k
below market most similar roles pay here above market

This role pays more than 73% of similar roles. Most pay $145,200–$214,125 — the blue band above. At the midpoint, this role pays about $211k versus about $180k 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 2164 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1129 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Manager, Data Scientist, US Card

Role Posting Log in to save

JOB TITLE: Manager, Data Scientist - US Card (Upmarket Acquisitions) The Manager, Data Scientist - US Card (Upmarket Acquisitions) joins the US Card Data Science team to build industry-leading machine learning models for underwriting decisions regarding new and existing credit card customers. This role involves owning the full model lifecycle, including development, deployment, monitoring, governance, and usage expansion. You will collaborate with data engineers, software engineers, and product managers to translate complex data into tangible business goals. You will leverage a technical stack including Python, Conda, AWS, H2O, Spark, and relational databases to analyze large volumes of numeric and textual data. Key responsibilities include building models for clustering, classification, sentiment analysis, time series, and deep learning to solve core credit card acquisition and management problems within a large-scale financial data environment.

Skills

Python AWS Spark H2O SQL Scala R Conda Relational Databases Machine Learning Deep Learning Time Series Sentiment Analysis Clustering Classification Statistical Modeling Big Data

What you'll do

  • Build machine learning models through all phases of development, including design, training, evaluation, and validation.
  • Deploy, monitor, and manage the full lifecycle of models to support core underwriting decisions.
  • Use Python, AWS, H2O, and Spark to extract insights from large volumes of numeric and textual data.
  • Retrieve and combine data from various sources and structures to perform large-scale data analysis.
  • Translate complex technical work into tangible business goals for stakeholders and cross-functional partners.
  • Develop and release model updates to improve the accuracy and robustness of credit card acquisitions.

What we're looking for

  • Must have a Bachelor's in a quantitative field plus 6 years of data analytics experience, a Master's/MBA 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 experience with clustering, classification, sentiment analysis, time series, and deep learning.
  • Must have experience building, validating, and backtesting models and interpreting metrics like confusion matrices or ROC curves.
  • Must have experience with Python, Conda, AWS, H2O, Spark, and other open-source tools.
  • Preferred: PhD in a STEM field plus 3 years of experience in data analytics.
  • Preferred: At least 1 year of experience working with AWS.
  • Preferred: At least 4 years of experience in Python, Scala, or R for large scale data analysis.
  • Preferred: At least 4 years of experience with machine learning.
  • Preferred: At least 4 years of experience with SQL.

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