Principal Associate, Data Science

2 weeks ago


McLean, United States Capital One Full time

Description

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 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 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.

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



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