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About the Role
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 AI for Data team builds and ships state of the art machine learning solutions to support data lifecycle within the enterprise. We partner with product, tech and design teams to deliver personalized experiences to our data and platform users to drive productivity and innovation. You will be the driving force to experiment, innovate and create next generation experiences powered by the latest emerging ML technologies.
Responsibilities
- 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 interpersonal skills to translate the complexity of work into tangible business goals
Requirements
- Currently have, or be in the process of obtaining a Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
- Currently have, or be in the process of obtaining 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 3 years of experience performing data analytics
- Currently have, or be in the process of obtaining a PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)
- 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
- Conda
- AWS
- H2O
- Spark
- Machine learning
- SQL
- Clustering
- Classification
- Sentiment analysis
- Time series
- Deep learning
Location
- McLean, VA
- New York, NY
- San Jose, CA
Work Type
- Full-time
Experience Level
- Principal
- Associate
Education Level
- Bachelor's Degree
- Master's Degree
- MBA
- PhD
Salary/Compensations
- McLean, VA: $161,800 - $184,600
- New York, NY: $176,500 - $201,400
- San Jose, CA: $176,500 - $201,400
Benefits
- Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
About the Company
- 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.
Equal Opportunity
- Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. 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.