Manager, Data Science
Capital One
Manager, Data Science
Job Description
Overview
Manager, Data Scientist, Retail Bank Data ScienceData 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 DescriptionThe Risk and Resiliency team in the Retail Bank builds the machine learning models that help our customers get an account, bank with the confidence that their accounts are secure, and get access to their money faster. We do data and model pipelining, machine learning, and well-managed model operations using Python, KFP, and ML libraries in our tech stacks.Role DescriptionIn this role, you will:Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver an experience that helps us book more customers
Take a well-managed approach to building customer-facing decision products while also bolstering our defenses with governed vendor tools that fill a niche and complement our own models
Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
Most critically, build connections with your partners to understand the fraud threats of today and tomorrow so you can devise a modeling roadmap that proxies fraud signal from our data, keeping the fraudsters out while making account opening a seamless experience for others
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.
Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing and deploying 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 and an AUPRC view. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
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
PhD in “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 5 years’ experience in Python, Scala, or R for large scale data analysis
At least 5 years’ experience with machine learning
At least 5 years’ experience with SQL
Previous experience with rare event prediction, especially fraud, for credit-like decisions strongly preferred
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.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.comCapital 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).
Locations
- McLean, Virginia McLean, VirginiaMcLean
Salary
80,000 - 135,000 USD / yearly
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- machine learningintermediate
- data analysisintermediate
- Python programmingintermediate
- model developmentintermediate
- SQLintermediate
- statistical modelingintermediate
- cloud computingintermediate
Required Qualifications
- Bachelor's Degree in quantitative field plus 6 years data analytics experience, or Master's plus 4 years, or PhD plus 1 year (experience)
- At least 1 year leveraging open source languages for data analysis (experience)
- At least 1 year working with machine learning (experience)
- At least 1 year utilizing relational databases (experience)
- PhD in STEM plus 3 years data analytics (preferred) (experience)
- At least 5 years Python/Scala/R experience (preferred) (experience)
- Previous experience with rare event prediction/fraud (preferred) (experience)
Responsibilities
- Partner with cross-functional team to deliver customer account experiences
- Build machine learning models through all development phases
- Develop modeling roadmap for fraud detection
- Use Python, KFP, and ML libraries for data pipelining
- Research emerging technologies and apply to problems
- Validate and backtest models
- Collaborate to understand fraud threats
Benefits
- general: comprehensive, competitive, and inclusive set of health, financial and other benefits
Answer 10 quick questions to check your fit for Manager, Data Science @ Capital One.
Related Books and Jobs
Manager, Data Science
Capital One
Manager, Data Science
Job Description
Overview
Manager, Data Scientist, Retail Bank Data ScienceData 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 DescriptionThe Risk and Resiliency team in the Retail Bank builds the machine learning models that help our customers get an account, bank with the confidence that their accounts are secure, and get access to their money faster. We do data and model pipelining, machine learning, and well-managed model operations using Python, KFP, and ML libraries in our tech stacks.Role DescriptionIn this role, you will:Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver an experience that helps us book more customers
Take a well-managed approach to building customer-facing decision products while also bolstering our defenses with governed vendor tools that fill a niche and complement our own models
Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
Most critically, build connections with your partners to understand the fraud threats of today and tomorrow so you can devise a modeling roadmap that proxies fraud signal from our data, keeping the fraudsters out while making account opening a seamless experience for others
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.
Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing and deploying 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 and an AUPRC view. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
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
PhD in “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 5 years’ experience in Python, Scala, or R for large scale data analysis
At least 5 years’ experience with machine learning
At least 5 years’ experience with SQL
Previous experience with rare event prediction, especially fraud, for credit-like decisions strongly preferred
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.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.comCapital 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).
Locations
- McLean, Virginia McLean, VirginiaMcLean
Salary
80,000 - 135,000 USD / yearly
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- machine learningintermediate
- data analysisintermediate
- Python programmingintermediate
- model developmentintermediate
- SQLintermediate
- statistical modelingintermediate
- cloud computingintermediate
Required Qualifications
- Bachelor's Degree in quantitative field plus 6 years data analytics experience, or Master's plus 4 years, or PhD plus 1 year (experience)
- At least 1 year leveraging open source languages for data analysis (experience)
- At least 1 year working with machine learning (experience)
- At least 1 year utilizing relational databases (experience)
- PhD in STEM plus 3 years data analytics (preferred) (experience)
- At least 5 years Python/Scala/R experience (preferred) (experience)
- Previous experience with rare event prediction/fraud (preferred) (experience)
Responsibilities
- Partner with cross-functional team to deliver customer account experiences
- Build machine learning models through all development phases
- Develop modeling roadmap for fraud detection
- Use Python, KFP, and ML libraries for data pipelining
- Research emerging technologies and apply to problems
- Validate and backtest models
- Collaborate to understand fraud threats
Benefits
- general: comprehensive, competitive, and inclusive set of health, financial and other benefits
Answer 10 quick questions to check your fit for Manager, Data Science @ Capital One.
Related Books and Jobs