Manager, Data Scientist- Capital One Data Insights and Analytics at Capital One in New York, NY

Job description

Manager, Data Scientist- Capital One Data Insights and Analytics 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: The Commercial Business Analysis (CBA) team partners with senior business leaders across Capital One to deliver on our vision to become the top middle market bank. As part of the team’s scope, we have a Capital One Data Insights and Analytics (CODIA) team dedicated to innovating with new sources of data to drive long-term value for our clients and the bank. We are currently expanding to integrate advanced machine learning models, modernize our data infrastructure, and identify new delivery models. In this role, you will: Partner with a cross-functional team of data scientists, business analysts, and relationship managers to deliver a product that creates real value for our clients. Partner with enterprise AI teams to build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation Conduct rigorous statistical analysis and back-testing against third-party data to integrate new datasets and mathematically validate insights. Modernize data infrastructure by developing tailored analytical tools on core enterprise data schemas and implementing real-time automated validation controls. Support long-term technical integrations for complex business projects, including comprehensive run-off analysis, underwriting automation, and advanced risk and portfolio management. The Ideal Candidate is: 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. 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. 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 6 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 4 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) plus 1 year of experience performing data analytics At least 1 year of experience