Manager, Data Science, US Card - Marketing

Capital One Financial

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
McLean, VANew York, NYCambridge, MA
Salary
$197,300–$225,100 / yr
Employment
Full-time
Posted
8 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 $190k
This role $211k
$142k $234k
below market most similar roles pay here above market

This role pays more than 66% of similar roles. Most pay $164,550–$214,900 — the blue band above. At the midpoint, this role pays about $211k versus about $190k 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 · Manager, Data Science, US Card - Marketing

Role Posting Log in to save

The Manager, Data Science - US Card - Marketing joins the Marketing Data Science team to develop advanced machine learning solutions for optimizing customer acquisition. You will build predictive models and reinforcement learning systems to deliver personalized customer experiences across the marketing funnel. Day-to-day responsibilities include designing, training, evaluating, and implementing models that define treatments for millions of customers in both analytical and production environments. You will utilize a technical stack including Python, Conda, AWS, H2O, Spark, SQL, XGBoost, scikit-learn, and statsmodels to analyze large volumes of numeric and textual data. The role involves solving complex business problems by translating model-driven insights into measurable growth, collaborating with cross-functional teams to provide targeted engagement and strategic decisions for the credit card product area.

What does a Data Science Manager earn?

Median $211200 from 43 postings across 13 companies.

See salary data

Skills

Python AWS Spark XGBoost scikit-learn statsmodels H2O SQL Scala R Conda Relational Databases Machine Learning Deep Learning Reinforcement Learning Time Series Sentiment Analysis Clustering Classification Statistical Modeling

What you'll do

  • Develop advanced machine learning solutions to optimize customer acquisition and personalize experiences.
  • Build machine learning models through all phases of development including design, training, evaluation, and implementation.
  • Use Python, AWS, Spark, and H2O to extract insights from large volumes of numeric and textual data.
  • Write custom Python libraries and utilize open-source libraries like XGBoost and scikit-learn for production environments.
  • Deploy supervised and reinforcement learning models to predict customer needs and recommend optimal solutions.
  • Translate complex technical work into tangible business goals for stakeholders and campaign managers.
  • Retrieve and combine data from various sources and structures to inform strategic business decisions.

What we're looking for

  • Must have a Bachelor's degree in a quantitative field plus 6 years of data analytics experience.
  • Must have a Master's degree in a quantitative field or an MBA with a quantitative concentration plus 4 years of data analytics experience.
  • Must have a PhD in a quantitative field plus 1 year of data analytics experience.
  • 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 with Python, Conda, AWS, H2O, Spark, and SQL (preferred).

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