Aitopics
Principal Quantitative Analyst - Model Risk
Aitopics, Mc Lean, Virginia, us, 22107
Capital One
You’re tenacious and driven, so the last place you want to work is some boring bank. Learn about careers at Capital One and view jobs here.Center 2 (19050), United States of America, McLean, VirginiaPrincipal Quantitative Analyst - Model RiskAt Capital One 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 Quantitative Analyst 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 cloud 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.As a Principal Quantitative Analyst within the Model Risk Office, you will be part of the Model Validation Team, working on the validation of stress testing models and Interest Rate and Liquidity Risk Management models. Validations cover all aspects of model development and performance and include forward-looking advancements in model sophistication and quality. You will enhance your technical and analytical skills, while also working closely with business leaders to influence business strategy. With a network of over 200 quantitative analysts and statisticians, we’ve created a dynamic environment with plenty of room for you to learn, grow, and realize your full potential.Responsibilities and Skills:Develop and implement validation strategies for statistical, financial, and other quantitative models used in stress testing, interest rate risk, liquidity risk and deposit funding.Assess the quality and risk of data, model methodologies, outputs, and processes.Develop alternative model approaches to assess model design and advance future capabilities.Apply deep expertise in econometric, statistical and machine learning methods to generate critical insights in assessing model risks and opportunities.Communicate clearly and concisely both verbally and through written communication via model validation reports and presentations.Identify opportunities to apply quantitative methods and automation solutions to improve business performance and process efficiencies.Expertise in quantitative analysis is central to our success in all markets. Our modelers thrive in a culture of mutual respect, excellence and innovation.Successful candidates will possess:Strong understanding of quantitative analysis methods in relation to financial institutions.Demonstrated track record in financial modeling, machine learning and econometric analysis.Experience utilizing model estimation tools.Ability to clearly communicate modeling results to a wide range of audiences.Drive to develop and maintain high quality and transparent model documentation.Strong written and verbal communication skills.Strong presentation skills.Ability to fully own the model development process: from conceptualization through data exploration, model selection, validation, deployment, business user training, and monitoring.Proficiency in key econometric and statistical techniques (such as predictive modeling, logistic regression, survival analysis, panel data models, design of experiments, decision trees, machine learning methods).Extensive experience in Python or other object-oriented language.Basic Qualifications:Currently has, or is in the process of obtaining a Bachelor’s Degree plus at least 5 years of experience in data analytics, or currently has, or is in the process of obtaining a Master’s Degree plus at least 3 years in data analytics, financial modeling or econometric modeling (can include Graduate School Research work) or currently has, or is in the process of obtaining PhD with an expectation that required degree will be obtained on or before the scheduled start date.At least 2 years of experience in data analytics or financial modeling or econometric modeling (can include Graduate School Research work).Preferred Qualifications:Master’s Degree or PhD in Statistics, Economics, Mathematics, Financial Engineering, Operations Research, Engineering, Finance, Physics or related discipline.2+ years of experience with data analysis.1+ year of experience manipulating and analyzing large data sets.1+ year of experience with Python, R or other statistical analyst software.Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace.
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You’re tenacious and driven, so the last place you want to work is some boring bank. Learn about careers at Capital One and view jobs here.Center 2 (19050), United States of America, McLean, VirginiaPrincipal Quantitative Analyst - Model RiskAt Capital One 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 Quantitative Analyst 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 cloud 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.As a Principal Quantitative Analyst within the Model Risk Office, you will be part of the Model Validation Team, working on the validation of stress testing models and Interest Rate and Liquidity Risk Management models. Validations cover all aspects of model development and performance and include forward-looking advancements in model sophistication and quality. You will enhance your technical and analytical skills, while also working closely with business leaders to influence business strategy. With a network of over 200 quantitative analysts and statisticians, we’ve created a dynamic environment with plenty of room for you to learn, grow, and realize your full potential.Responsibilities and Skills:Develop and implement validation strategies for statistical, financial, and other quantitative models used in stress testing, interest rate risk, liquidity risk and deposit funding.Assess the quality and risk of data, model methodologies, outputs, and processes.Develop alternative model approaches to assess model design and advance future capabilities.Apply deep expertise in econometric, statistical and machine learning methods to generate critical insights in assessing model risks and opportunities.Communicate clearly and concisely both verbally and through written communication via model validation reports and presentations.Identify opportunities to apply quantitative methods and automation solutions to improve business performance and process efficiencies.Expertise in quantitative analysis is central to our success in all markets. Our modelers thrive in a culture of mutual respect, excellence and innovation.Successful candidates will possess:Strong understanding of quantitative analysis methods in relation to financial institutions.Demonstrated track record in financial modeling, machine learning and econometric analysis.Experience utilizing model estimation tools.Ability to clearly communicate modeling results to a wide range of audiences.Drive to develop and maintain high quality and transparent model documentation.Strong written and verbal communication skills.Strong presentation skills.Ability to fully own the model development process: from conceptualization through data exploration, model selection, validation, deployment, business user training, and monitoring.Proficiency in key econometric and statistical techniques (such as predictive modeling, logistic regression, survival analysis, panel data models, design of experiments, decision trees, machine learning methods).Extensive experience in Python or other object-oriented language.Basic Qualifications:Currently has, or is in the process of obtaining a Bachelor’s Degree plus at least 5 years of experience in data analytics, or currently has, or is in the process of obtaining a Master’s Degree plus at least 3 years in data analytics, financial modeling or econometric modeling (can include Graduate School Research work) or currently has, or is in the process of obtaining PhD with an expectation that required degree will be obtained on or before the scheduled start date.At least 2 years of experience in data analytics or financial modeling or econometric modeling (can include Graduate School Research work).Preferred Qualifications:Master’s Degree or PhD in Statistics, Economics, Mathematics, Financial Engineering, Operations Research, Engineering, Finance, Physics or related discipline.2+ years of experience with data analysis.1+ year of experience manipulating and analyzing large data sets.1+ year of experience with Python, R or other statistical analyst software.Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace.
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