Principal Associate, Data Scientist - US Card DFS Acquisitions

Principal Associate, Data Scientist - US Card DFS Acquisitions 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 DFS Acquisitions Integration Data Science team builds industry leading machine learning models to empower core underwriting decisions in the acquisitions of a new credit card customer. The team is responsible for meeting model risk standards and enabling COF model use in acquisition area integration policies; supporting increased scaling volume by bringing key DFS insights (data, features, or models) into the COF ecosystem; building or refitting key models combining COF and Discover populations to drive value. We collaborate closely with a wide range of cross functional partner teams – data engineers, platforms engineers, product managers, credit and business analysts – to deliver the solutions from ideation to implementation. We are a team of model developers, who own the full life cycle of our models – development, deployment, monitoring, governance, and ongoing usage expansion and releases. We are also a team of creative problem solvers, who challenge the status quo on a continuous basis and are devoted to innovation to keep making our models more dynamic, adaptive, robust, and ultimately, smarter. Role Description 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: A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond. 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 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’ experience in Python, Scala, or R. At least 3 years’ experience with machine learning. At least 3 years’ experience with SQL. Benefits Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $161,800 - $184,600 for Principal Associate, Data Science Riverwoods, IL: $147,100 - $167,900 for Principal Associate, Data Science This role is eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Equal Opportunity Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23‑A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901‑4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. Contact For technical support or questions about Capital One's recruiting process, please send an email to View email address on click.appcast.io Disclaimer Capital One does not provide, endorse nor guarantee and is not liable for third‑party products, services, educational tools or other information available through this site. #J-18808-Ljbffr