Principal Associate, Data Scientist
Not Disclosed•Full-TimeOn-site
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
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 in 1988. Fast-forward a few years, and this innovation and our passion for data have 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 leading the next wave of disruption at a whole new scale. We use the latest in computing and machine learning technologies, operating across billions of customer records to unlock big opportunities that help everyday people save money, time, and agony in their financial lives.
The Valuations team is a multi-disciplinary group focused on the development, deployment, model governance, and risk management of valuations models in credit underwriting. Our mission is to bring real-time, personalized offers to our customers through innovation and optimization in our valuations modeling system. We work with partners from across the enterprise to build data and modeling products that enable best-in-class credit analysis and customer-facing decisioning.
Underwriting innovation is a key source of Capital One's competitive advantage—it is in our DNA. Our team continues to push that competitive edge through the use of new data and advanced modeling techniques. Building the capabilities the enterprise needs is a challenging long-term project; the prize is big, and there are plenty of opportunities to deliver value along the way.
Our team's work is both intellectually demanding and highly collaborative. You will work across LOB underwriting teams, various DS/DA teams, enterprise platform teams, enterprise data teams, tech teams, legal and compliance teams, consumer credit risk management, the model risk office, and enterprise data risk management.
Great team culture is profoundly important to us: we want everyone to feel they are doing meaningful and enjoyable work. We are informal and indifferent to hierarchy, actively encourage new ideas and new ways of doing things, and respect each other's perspectives and preferences.
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. You will leverage a broad stack of technologies to reveal insights hidden within huge volumes of numeric and textual data. Your work will involve building machine learning models through all phases of development, from design through training, evaluation, validation, and implementation. You will also flex your interpersonal skills to translate the complexity of your work into tangible business goals.
The ideal candidate is customer-first, innovative, creative, technical, statistically-minded, and a data guru. You will thrive on bringing definition to big, undefined problems, stay current on state-of-the-art methods, and be comfortable with open-source languages and cloud computing platforms.
This role is expected to accept applications for a minimum of 5 business days. No agencies, please.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. We promote a drug-free workplace and will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws.
If you require an accommodation to apply, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information provided will be kept confidential.
Work model: On-site
NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
New York, New York
Master's Degree in a STEM field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in a STEM field. 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.
Skills: Python, Scala, R, Aws, Spark, Conda, SQL, Machine Learning, Statistical Modeling, Clustering.
Education: Bachelor's Degree in a quantitative field required with 5 years of experience; Master's Degree in a quantitative field or MBA with quantitative concentration required with 3 years of experience; PhD in a quantitative field required.
