Principal Associate, Data Science - US Card Fraud Authentication Team

Principal Associate, Data Science - US Card Fraud Authentication Team

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.

Team Description:

The US Card Fraud Authentication Data Science team operates at the critical intersection of fraud defenses and customer experience. We leverage a modern tech stack - including Python, AWS, Spark, H2O, and SQL - to analyze complex datasets and optimize authentication decisioning at Capital One. Our mission is to stay ahead of evolving fraud patterns through a combination of rigorous quantitative analysis and the development of scalable machine learning solutions. 

In this role, you will:

• Dive into ambiguous fraud landscapes to identify opportunities where machine learning can drive value. You will help design the technical vision for our authentication workflows, evaluating feasibility and prototyping innovative ML solutions

• Harness our big data ecosystem (Spark, AWS, and more) to uncover hidden patterns in massive datasets, turning raw information into actionable fraud-prevention strategies

• Partner with a cross-functional team of business stakeholders, data scientists, software engineers, and product managers to deliver a product customers love

• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation

• Bridge the gap between technical complexity and business strategy, articulating the "why" behind your solutions to influence stakeholders and achieve tangible business goals

The Ideal Candidate is:

• Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.

• 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 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.

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 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, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)

Preferred Qualifications:

• Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)

• At least 1 year of experience working with AWS

• At least 3 years of experience in Python, Scala, or R

• At least 3 years of experi