Principal Data Scientist - Consumer Credit Risk Management Models and Data
Capital One, McLean, VA, United States
Center 2 (19050), United States of America, McLean, Virginia
Principal Data Scientist - Consumer Credit Risk Management Models and Data
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 the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.
As a Data Scientist at Capital One, you'll be part of a team that's 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 the big opportunities that help everyday people save money, time and agony in their financial lives.
Team Description
Have you ever seen the headline in the news Banks Pass Federal Reserve Stress Tests and wondered how Capital One determines how much savings (or "capital") it needs? Or maybe how we analyze the potential impact of the next recession? At the heart of these questions are sophisticated econometric loss models that help us understand the ways in which the economy impacts our loan portfolios and guide strategic decision making at the highest levels of Capital One.
In the Consumer Credit Risk Management Models and Data Team, we blend cutting-edge quantitative methods, with a deep understanding of our business, data, and regulatory environment to build and deploy predictive models for losses, account volumes and outstanding balances. These models drive key strategic decisions for loss allowances, stress testing, and capital allocation as well as informing our earnings calls and recession preparedness.
If this sounds interesting to you, join us! As a Data Scientist on the deployment & platform side of the team, you'll be at the forefront helping us to usher in the next wave of disruption by using the latest technology to deploy, optimize and modernize model pipelines and execution platforms that enable machine learning models to provide powerful insights about our portfolio and growth opportunities through new data sources. You will partner with best-in-class data scientists, analysts, and engineers to innovate solutions that directly impact the company's bottom line in a meaningful way. You will do it all in a collaborative environment that values your insight, encourages you to take on new responsibilities, promotes continuous learning, and rewards innovation.
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 - Python, Conda, AWS, H2O, Spark, and more - to reveal the insights hidden within huge volumes of numeric and textual data
Optimize and deploy 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, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers.
A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond.
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.
Statistically-minded. You've built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
A data guru. "Big data" doesn't faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science.
Basic Qualifications:
Currently has, or is in the process of obtaining a Bachelor's Degree plus 5 years of experience in data analytics, or currently has, or is in the process of obtaining a Master's Degree plus 3 years in data analytics, or currently has, or is in the process of obtaining PhD, with an expectation that required degree will be obtained on or before the scheduled start date
At least 1 year of experience in open source programming languages for large scale data analysis
At least 1 year of experience with machine learning
At least 1 year of experience with relational databases
Preferred Qualifications:
Master's Degree in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in "STEM" field (Science, Technology, Engineering, or Mathematics)
At least 1 year of experience working with AWS
At least 3 years' experience in Python, Scala, or R
At least 3 years' experience with machine learning
At least 3 years' experience with SQL
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex (including pregnancy, childbirth or related medical conditions), race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity, gender reassignment, citizenship, immigration status, protected veteran status, or any other basis prohibited under applicable federal, state or local law.
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