Manager, Data Scientist - Business Cards & Payments
Capital One National Association, New York, NY, United States
Manager, Data Scientist – Business Cards & Payments
Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and relational databases, cutting‑edge technology in 1988. Fast‑forward a few years, and this innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in data‑driven decision‑making.
As a Data Scientist at Capital One, you’ll be part of a team that is leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock big opportunities that help everyday people save money, time, and agony in their financial lives.
Team Description
The Business Card & Payments Data Science team builds industry‑leading machine learning models to power credit underwriting decisions. These models support emerging business card product strategies and are a key part of our credit infrastructure. The team builds full‑spectrum modeling solutions across customer life cycles, engaging in strong collaboration with business stakeholders, product, and software/ML engineering teams to problem‑solve and develop innovative approaches that define our long‑term modeling strategy.
Role Description
In this role, you will:
Partner with a cross‑functional team of data scientists, software engineers, and product managers to deliver a product customers love.
Leverage a broad stack of technologies—including Python, Conda, AWS, H2O, Spark, and more—to reveal the insights hidden within huge volumes of numeric and textual data.
Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
The Ideal Candidate is:
Customer first. You love the process of analyzing and creating, and you share our passion for doing the right thing. You know that at the end of the day it’s about making the right decision for our customers.
Technical. You’re comfortable with open‑source languages and are passionate about developing further. You have hands‑on experience developing data science solutions using open‑source tools and cloud computing platforms.
Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
Statistically-minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve and have experience with clustering, classification, sentiment analysis, time series, and deep learning.
Basic Qualifications
Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:
A Bachelor’s Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics.
A Master’s Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics.
A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related 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.
Preferred Qualifications
PhD in a STEM field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics.
At least 1 year of experience working with AWS.
At least 4 years of experience in Python, Scala, or R for large‑scale data analysis.
At least 4 years of experience with machine learning.
At least 4 years of experience with SQL.
Salary and Compensation
McLean, VA: $197,300 – $225,100
New York, NY: $215,200 – $245,600
This role is also eligible to earn performance‑based incentive compensation, which may include cash bonuses and/or long‑term incentives (LTI). Incentives could be discretionary or non‑discretionary depending on the plan.
Benefits
Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well‑being.
EEO Statement
Capital One is an equal‑opportunity employer (EOE, including disability/veteran) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace and considers applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries.
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Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and relational databases, cutting‑edge technology in 1988. Fast‑forward a few years, and this innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in data‑driven decision‑making.
As a Data Scientist at Capital One, you’ll be part of a team that is leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock big opportunities that help everyday people save money, time, and agony in their financial lives.
Team Description
The Business Card & Payments Data Science team builds industry‑leading machine learning models to power credit underwriting decisions. These models support emerging business card product strategies and are a key part of our credit infrastructure. The team builds full‑spectrum modeling solutions across customer life cycles, engaging in strong collaboration with business stakeholders, product, and software/ML engineering teams to problem‑solve and develop innovative approaches that define our long‑term modeling strategy.
Role Description
In this role, you will:
Partner with a cross‑functional team of data scientists, software engineers, and product managers to deliver a product customers love.
Leverage a broad stack of technologies—including Python, Conda, AWS, H2O, Spark, and more—to reveal the insights hidden within huge volumes of numeric and textual data.
Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
The Ideal Candidate is:
Customer first. You love the process of analyzing and creating, and you share our passion for doing the right thing. You know that at the end of the day it’s about making the right decision for our customers.
Technical. You’re comfortable with open‑source languages and are passionate about developing further. You have hands‑on experience developing data science solutions using open‑source tools and cloud computing platforms.
Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
Statistically-minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve and have experience with clustering, classification, sentiment analysis, time series, and deep learning.
Basic Qualifications
Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:
A Bachelor’s Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics.
A Master’s Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics.
A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related 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.
Preferred Qualifications
PhD in a STEM field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics.
At least 1 year of experience working with AWS.
At least 4 years of experience in Python, Scala, or R for large‑scale data analysis.
At least 4 years of experience with machine learning.
At least 4 years of experience with SQL.
Salary and Compensation
McLean, VA: $197,300 – $225,100
New York, NY: $215,200 – $245,600
This role is also eligible to earn performance‑based incentive compensation, which may include cash bonuses and/or long‑term incentives (LTI). Incentives could be discretionary or non‑discretionary depending on the plan.
Benefits
Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well‑being.
EEO Statement
Capital One is an equal‑opportunity employer (EOE, including disability/veteran) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace and considers applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries.
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