Principal Data Scientist

Principal Associate, Data Science - Model Risk OfficeData 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 Capital One's Model Risk Office, we defend the company against model failures and find new ways of making better decisions with models. We use our statistics, software engineering, and business expertise to drive the best outcomes in both Risk Management and the Enterprise. We understand that we can't prepare for tomorrow by focusing just on today, so we invest in the future: investing in new skills, building better tools, and maintaining a network of trusted partners. We learn from past mistakes, and develop increasingly powerful techniques to avoid their repetition.Role DescriptionIn this role, you will:Partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with modelsLeverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual dataLead teams of data scientists who build machine learning models to challenge current champion models that are deployed in production and contribute to the model governance framework for the next generation of modelsLead validation of a wide variety of models across multiple business domains and flex your interpersonal skills to present how identified model risks could impact the business to executives.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.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.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.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 analyticsA 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 analyticsA 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 AWSAt least 3 years' experience in Python, Scala, or RAt least 3 years' experience with machine learningAt least 3 years' experience with SQL