NYC Staffing
Principal Associate, Quantitative Analyst - Commercial Credit Mod...
NYC Staffing - New York, New York, United States, 10001
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Overview
Principal Associate, Quantitative Analyst
At 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. Capital One's Commercial Bank Team has a $100B+ loan portfolio that has grown organically and via acquisitions, covering C&I, CRE, and Structured Products. On this team, you'll get an opportunity to solve a diverse set of problems with a diverse set of tools. In some settings, you'll leverage open source programming or cloud computing to predict credit risk loss across multi-million record datasets using statistical techniques. In other settings, you'll get the opportunity to use completely different skill sets, blending business thinking with simulation and quantitative tools when forecasting rare or unprecedented events. It's a growing team full of exciting opportunities to solve a range of complex problems. Responsibilities And Skills:
Develop credit risk models for various business applications, including internal risk rating, loss forecasting, stress testing, pricing for commercial real estate and structured products Partner with the business analyst team to enhance modeling and analytical frameworks to generate business insights and drive changes Proactively identify opportunities to apply quantitative methods and automation solutions to improve model performance and process efficiencies Collaborate with implementation and data infrastructure team to build cloud-based solutions for model deployment, monitoring, and maintenance Work effectively with challenge functions to ensure prompt and comprehensive support for model governance Successful Candidates Will Possess:
Excellent coding skills in Python (must-have) and/or R (good to have) with self-drive to create accurate, efficient, and organized code using industry best practices Hands-on experience utilizing data analysis and visualization packages to gain insights from large datasets Deep understanding of quantitative analysis methods with practical considerations under the context of financial institutions and/or commercial bank lending Demonstrated track-record in applying statistical, machine learning and/or econometric analysis to solve business problems Drive to develop and maintain high quality and transparent model documentation Strong communication skills both written and verbal, and storytelling skills for presentations Experience with simulation-based modeling approach, stochastic processes, etc Appreciation for processes, controls, and good governance 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 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 with Python, R or other statistical analyst software 1 year of experience manipulating and analyzing large data sets
At 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. Capital One's Commercial Bank Team has a $100B+ loan portfolio that has grown organically and via acquisitions, covering C&I, CRE, and Structured Products. On this team, you'll get an opportunity to solve a diverse set of problems with a diverse set of tools. In some settings, you'll leverage open source programming or cloud computing to predict credit risk loss across multi-million record datasets using statistical techniques. In other settings, you'll get the opportunity to use completely different skill sets, blending business thinking with simulation and quantitative tools when forecasting rare or unprecedented events. It's a growing team full of exciting opportunities to solve a range of complex problems. Responsibilities And Skills:
Develop credit risk models for various business applications, including internal risk rating, loss forecasting, stress testing, pricing for commercial real estate and structured products Partner with the business analyst team to enhance modeling and analytical frameworks to generate business insights and drive changes Proactively identify opportunities to apply quantitative methods and automation solutions to improve model performance and process efficiencies Collaborate with implementation and data infrastructure team to build cloud-based solutions for model deployment, monitoring, and maintenance Work effectively with challenge functions to ensure prompt and comprehensive support for model governance Successful Candidates Will Possess:
Excellent coding skills in Python (must-have) and/or R (good to have) with self-drive to create accurate, efficient, and organized code using industry best practices Hands-on experience utilizing data analysis and visualization packages to gain insights from large datasets Deep understanding of quantitative analysis methods with practical considerations under the context of financial institutions and/or commercial bank lending Demonstrated track-record in applying statistical, machine learning and/or econometric analysis to solve business problems Drive to develop and maintain high quality and transparent model documentation Strong communication skills both written and verbal, and storytelling skills for presentations Experience with simulation-based modeling approach, stochastic processes, etc Appreciation for processes, controls, and good governance 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 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 with Python, R or other statistical analyst software 1 year of experience manipulating and analyzing large data sets