Senior Associate, Data Scientist - Small Business Bank (Fraud)

Senior Associate, Data Scientist - Small Business Bank (Fraud)

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.

In Small Business Banking (SBB), we put ourselves in the shoes of our small business owners to understand their needs and help them succeed. The SBB Data Science team has a relentless focus on the craft of data sciences spanning statistics, machine learning and emerging machine learning  domains. We develop innovative solutions that help our business partners design and deliver products and policies that meet small businesses’ needs and expectations in financial services. The Fraud DS team builds the machine learning models that help protect our customers and Capital One against fraudsters. You will work with cross functional teams including business analysts, product managers and engineers to identify and enable intelligent decisions.

In this role, you will:

• Partner with a cross-functional team of data scientists, business analysts, 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

• 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

• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

The Ideal Candidate is:

• Statistically-minded. You’ve built models, validated them, and back tested 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.

• 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.

• 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.

• 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.

• 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.

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 2 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

Preferred Qualifications:

• Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics), or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)

• At least 2 years of experience with machine learning

• At least 2 years of experience in Python, Dask, Scala, or R

• At least 2 years of experience with SQL 

• At least 1 ye