Principal Associate, Data Scientist - US Card DFS Acquisitions

Principal Associate, Data Scientist - US Card DFS Acquisitions

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 US Card DFS Acquisitions Integration Data Science team builds industry leading machine learning models to empower core underwriting decisions in the acquisitions of a new credit card customer. The team is responsible for meeting model risk standards and enabling COF model use in acquisition area integration policies; supporting increased scaling volume by bringing key DFS insights (data, features, or models) into the COF ecosystem;

building or refitting key models combining COF and Discover populations to drive value. We collaborate closely with a wide range of cross functional partner teams – data engineers, platforms engineers, product managers, credit and business analysts – to deliver the solutions from ideation to implementation. We are a team of model developers, who own the full life cycle of our models – development, deployment, monitoring, governance, and ongoing usage expansion and releases.

We are also a team of creative problem solvers, who challenge the status quo on a continuous basis and are devoted to innovation to keep making our models more dynamic, adaptive, robust, and ultimately, smarter.

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.

• 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 faze 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.

• 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 5 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 3 years of experience performing data analytics.
  • A PhD in a quantitative field (Statistics, Economics,…