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Data Science Manager, Quantitative Marketing

Job ID R77342 Updated date 06/05/2019 Location San Francisco, California
201 Third Street (61049), United States of America, San Francisco, California

At Capital One, we’re building a leading information-based technology company. Still founder-led by Chairman and Chief Executive Officer Richard Fairbank, Capital One is on a mission to help our customers succeed by bringing ingenuity, simplicity, and humanity to banking. We measure our efforts by the success our customers enjoy and the advocacy they exhibit. We are succeeding because they are succeeding.                                               

Guided by our shared values, we thrive in an environment where collaboration and openness are valued. We believe that innovation is powered by perspective and that teamwork and respect for each other lead to superior results. We elevate each other and obsess about doing the right thing. Our associates serve with humility and a deep respect for their responsibility in helping our customers achieve their goals and realize their dreams. Together, we are on a quest to change banking for good.

Data Science Manager, Quantitative Marketing

At Capital One, data is at the center of everything we do. When we launched 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 Manager of Data Science 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 distributed computing technologies and operating across billions and billions of customer transactions to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.

On any given day you’ll be:

-Determining, analyzing and maintaining marketing metrics to identify cause-effect relationships between marketing actions and behavioral and attitudinal outcomes.

-Guiding the development of econometric and optimization tools for marketing mix modeling.

-Extracting and manipulating large data-sets using modern technologies.

-Assessing the validity and rigor of new data sources and their use in quantitative marketing.

-Creating machine learning models from development through testing and validation to our 30+ million customers in production
-Designing rich data visualizations to communicate complex ideas to customers or company leaders
-Investigating the impact of new technologies on the future of mobile banking and the financial world of tomorrow

The Ideal Candidate will be…
-Curious. You ask why, you explore, and you are not afraid to blurt out your crazy idea. You probably have experience with Kaggle competitions.

-Wrangler. You know how to move data around, from a database or an API, through a transformation or two, a model and into human-readable form (ROC curve, Excel chart, map, d3 visualization, Tableau, etc.). You probably know Python, Java, R, Storm, Julia, SQL, Matlab, Mahout, or think everything can be done in a Perl one-liner.
-Do-er. You have a bias toward action, you try things, and sometimes you fail. Expect to tell us what you’ve shipped and what’s flopped.
-Fearless. Big, undefined problems and petabytes don't frighten you. You can work at a tiny crack until you've broken open the whole nut.
Basic Qualifications:

-Experience in applied research with a focus on consumer behavior and marketing effectiveness.
-Advanced Degree in Economics, Statistics, Computer Science, Machine Learning, Applied Mathematics,

 Operations Research, Engineering, or similar quantitative field.

-Solid knowledge of applied statistics such as sampling, experimental design, and causal modeling

-Knowledge of modern machine learning techniques.

-Experience with A/B and multivariate test design and implementation.

-Knowledge of Bayesian statistics is a plus.
-At least 2 years’ experience in open source programming languages for large scale data analysis
-At least 2 years’ experience with machine learning
-At least 2 years’ experience with relational databases
-At least 2 years’ experience with SQL

Preferred Qualifications:
- PhD
-3+ years’ experience in open source programming languages for large scale data analysis
-3+ years’ experience with machine learning
-3+ years’ experience with relational databases
-3+ years’ experience with SQL

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.

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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).