Manager, Data Scientist - Credit Review

Manager, Data Scientist - Credit ReviewData 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 DescriptionIn Capital One's Credit Review Models, Data and Innovative Solutions team, 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 on today, so we invest in the future: investing in new skills, building better tools, and maintaining a network of trusted partners.We partner with best-in-class data scientists, analysts, credit risk management experts, and engineers to innovate solutions that directly impact the company's bottom line in a meaningful way. We do it all in a collaborative environment that values individual insight, encourages each associate to take on new responsibilities, promotes continuous learning, and rewards innovation.Role DescriptionIn this role, you will:Leverage a broad stack of technologies, such as, Python, Conda, AWS, H2O, Spark, and more, to reveal the insights hidden within huge volumes of numeric and textual dataBuild statistical and machine learning models to challenge the models in productionFlex your interpersonal skills to translate the complexity of your work into tangible business goalsPartner with a cross-functional team of data scientists, credit risk experts, and product managers to deliver a product customers loveThe Ideal Candidate is: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.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.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 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 4 years of experience performing data analyticsA 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 analyticsAt least 1 year of experience leveraging open source programming languages for large scale data analysisAt least 1 year of experience working with machine learningAt least 1 year of experience utilizing relational databasesPreferred Qualifications:PhD in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analyticsAt least 4 years' experience in Python, Scala, or R for large scale data analysisAt least 4 years' experience with machine learningAt least 4 years' experience with predictive modelingCapital One will consider sponsoring a new qualified applicant for employment authorization for this position.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.Charlotte, NC: $179,400 - $204,700 for Mgr, Data ScienceMcLean, VA: $197,300 - $225,100 for Mgr, Data ScienceNew York, NY: $215,200 - $245,600 for Mgr, Data SciencePlano, TX: $179,400 - $204,700 for Mgr, Data ScienceRichmond, VA: $179,400 - $204,700 for Mgr, Data ScienceRiverwoods, IL: $179,400 - $204,700 for Mgr, Data ScienceCandidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.This role is also 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.Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.