Manager, Data Scientist - Business Cards & Payments

Capital One Financial

Confirmed live yesterday Low trust

Quick summary

Work type: On-site
Location: New York, NYMcLean, VA
Salary: $197,300–$225,100 / yr
Posted: 142 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $170k

This role $211k

$115k most similar roles pay here $237k

This role pays more than 74% of similar roles. Most pay $126,800–$213,817 — the shaded band above. At the midpoint, this role pays about $211k versus about $170k 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.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Manager, Data Scientist - Business Cards & Payments

Role Posting Log in to save

Manager, Data Scientist - Business Cards & Payments joins the Business Card & Payments Data Science team to build machine learning models that power credit underwriting decisions and support emerging product strategies. This role involves collaborating with cross-functional teams of data scientists, software engineers, and product managers to develop full-spectrum modeling solutions across customer life cycles. You will be responsible for building models through all phases of development, including design, training, evaluation, validation, and implementation. To uncover insights within large volumes of numeric and textual data, you will utilize a technology stack including Python, Conda, AWS, H2O, Spark, and relational databases. The work focuses on solving complex credit infrastructure problems by applying advanced techniques such as clustering, classification, sentiment analysis, time series, and deep learning to improve the accuracy of automated financial decision-making processes.

Skills

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

What you'll do

Build machine learning models for credit underwriting decisions across various customer life cycles.
Develop full-spectrum modeling solutions from initial design through training, evaluation, validation, and implementation.
Analyze large volumes of numeric and textual data using Python, Spark, H2O, and AWS.
Translate complex technical findings into tangible business goals for non-technical stakeholders.
Solve large, undefined problems by identifying innovative approaches to credit infrastructure challenges.
Manage the end-to-end development of models including clustering, classification, sentiment analysis, and time series.
Utilize relational databases and open-source tools to uncover insights from massive datasets.

What we're looking for

A Bachelor's degree in a quantitative field plus 6 years of experience performing data analytics.
A Master's degree in a quantitative field or an MBA with a quantitative concentration plus 4 years of experience performing data analytics.
A PhD in a quantitative field plus 1 year of experience performing data analytics.
At least 1 year of experience leveraging open source programming languages for large scale data analysis.
At least 1 year of experience working with machine learning.
At least 1 year of experience utilizing relational databases.
Experience building and validating models using techniques like clustering, classification, sentiment analysis, time series, and deep learning.
Proficiency in a broad stack including Python, Conda, AWS, H2O, Spark, and SQL.

Save