EXPERIENCE

Manager

TEAM DESCRIPTION

The 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.

BASIC QUALIFICATIONS:

• 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

PREFERRED QUALIFICATIONS:

• 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
  Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
  The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
  McLean, VA: $193,400 - $220,700 for Mgr, Data Science
  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.
  This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
  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.
  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. 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).
  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!

IN 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