Manager, Data Scientist - US Card (Resiliency Intelligence)

Manager, Data Scientist - US Card (Resiliency Intelligence) 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 Resiliency Intelligence team builds the machine learning models that help our customers regain financial stability and drive business value through personalized solutions at scale. This mission is critical for supporting customers facing financial hardship, ensuring they have the personalized resources needed to resolve outstanding balances and reclaim their financial health. To accomplish this, we leverage supervised and reinforcement learning to predict customer needs and recommend optimal solutions. We develop custom Python libraries and utilize a tech stack featuring XGBoost, scikit‑learn, and statsmodels. Our models define treatments for millions of customers daily, with your code running across both analytical and production environments. Our machine learning solutions are a key value generator, meaningfully impacting the income of the US Card business. Join our growing team as we innovate new ways to use data and technology to unlock opportunities that help everyday people improve their financial lives. 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 The Ideal Candidate is: 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. 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 or MBA with a quantitative concentration plus 4 years of experience performing data analytics A PhD in a quantitative field plus 1 year of experience performing data analytics At least 1 year of experience leveraging open source programming languages for large scale data analysis At least 1 year of experience working with machine learning At least 1 year of experience utilizing relational databases Preferred Qualifications: PhD in a STEM field plus 3 years of experience in data analytics At least 1 year of experience working with AWS At least 4 years’ experience in Python, Scala, or R for large scale data analysis At least 4 years’ experience with machine learning #J-18808-Ljbffr