Senior Manager, Data Science - Consumer Credit Card
Category Data Science
Experience Sr. Manager
Primary Address McLean, Virginia
OverviewCenter 1 (19052), United States of America, McLean, VirginiaSenior Manager, Data Science - Consumer Credit Card
Senior Manager - Data Scientist
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.
The Card DS team is working continuously to innovate the way we handle data to transform every corner of our business through analytics, infrastructure, valuations, and strategy. We build human-centric experiences for moments that matter throughout the customer journey, and we do this by harnessing data, technology, and talent to propel our business forward. To accomplish this goal, we design, build, and maintain the appropriate solutions we use across Card to make smart, informed decisions. We use the latest techniques in machine learning to build predictive models and utilize the advantages of large-scale data and cloud-based processing. Through these techniques, we extract relevant insights from data to tackle a huge variety of business problem
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
- 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, 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.
- Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
- 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.
- 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 phase 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.
- Bachelor’s Degree plus 7 years of experience in data analytics, or Master’s Degree plus 5 years of experience in data analytics, or PhD plus 2 years of experience in data analytics
- At least 3 years’ experience in open source programming languages for large scale data analysis
- At least 3 years’ experience with machine learning
- At least 3 years’ experience with relational databases
- PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 4 years of experience in data analytics
- At least 1 year of experience working with AWS
- At least 1 year of experience managing people
- At least 5 years’ experience in Python, Scala, or R for large scale data analysis
- At least 5 years’ experience with machine learning
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.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, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
How We Hire
We take finding great coworkers pretty seriously.
Step 1 Apply
It only takes a few minutes to complete our application and assessment.
Step 2 Screen and Schedule
If your application is a good match you’ll hear from one of our recruiters to set up a screening interview.
Step 3 Interview(s)
Now’s your chance to learn about the job, show us who you are, share why you would be a great addition to the team and determine if Capital One is the place for you.
Step 4 Decision
The team will discuss — if it’s a good fit for us and you, we’ll make it official!
How to Pick the Perfect Career Opportunity
Overwhelmed by a tough career choice? Read these tips from Devon Rollins, Senior Director of Cyber Intelligence, to help you accept the right offer with confidence.
Your wellbeing is our priority
Our benefits and total compensation package is designed for the whole person. Caring for both you and your family.
Healthy Body, Healthy Mind
You have options and we have the tools to help you decide which health plans best fit your needs.
Save Money, Make Money
Secure your present, plan for your future and reduce expenses along the way.
Time, Family and Advice
Options for your time, opportunities for your family, and advice along the way. It’s time to BeWell.
Here’s how the team fits together. We’re big on growth and knowing who and how coworkers can best support you.
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