Capital One National Association
Manager, Data Science - Automation Excellence
Capital One National Association, Mc Lean, Virginia, us, 22107
Center 2 (19050), United States of America, McLean, Virginia
Manager, Data Science - Automation Excellence
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 Retail Bank Data Science team has a relentless focus on innovation with a passion for improving customer experiences. For that, our team writes lots of custom Python and Bash code to develop and deploy advanced AI/ML techniques. We care very deeply about doing things the right way, automating, standardizing, and removing barriers to accelerate our speed to market. Whether it’s big or small data, our team’s ability to turn that data into insights and insights into great decisions hinges upon the code that makes it all work. This role will be the catalyst that drives data science culture towards adopting the best mix of internal and external platforms, tools, and practices to deliver model-based insights to our customers faster than ever before. Role Description
In this role, you will: Lead our code quality initiative, defining the vision and roadmap for the effort and carrying it out across the Retail Bank. Present the code quality initiative and progress to both leaders and peers within the Bank and across the company, while driving adoption throughout the team. Evaluate Machine Learning pipelines and Enterprise offerings to coach the team towards making the best development and deployment decisions for their use cases. Leverage expertise on platforms and software best practices to enable and improve data scientists’ code resiliency and performance. Develop and maintain software tools, playbooks, and documentation to further drive engineering practices that rapidly lead to high performance models. Curate or create training for software development best practices for data scientist mastery with tools used on model build and execution platforms. The Ideal Candidate is:
Technical.
You’re experienced with open source languages and how they’re used in artificial intelligence and machine learning. You understand what can and should be automated and standardized. A Teacher.
You know your way around a complex, collaborative code base, but you still remember what it was like writing your first script. A Self-Starter.
You will own the mission to define the structure that leads our team to eliminate errors and make work exceptionally transferable. Detail Oriented.
You appreciate elegance and efficiency in code. You believe the little things add up. Collaborative.
You work well with leaders and fellow data scientists to balance code quality needs with existing project timelines. Basic Qualifications:
Currently has, or is in the process of obtaining a Bachelor’s Degree plus 6 years of experience in data analytics, or currently has, or is in the process of obtaining a Master’s Degree plus 4 years of experience in data analytics, or currently has, or is in the process of obtaining PhD plus 1 year of experience in data analytics. At least 2 years’ experience in open source programming languages for large scale data analysis. At least 2 years’ experience with machine learning. At least 2 years’ experience with relational databases. Preferred Qualifications:
PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics. At least 1 year of experience working with AWS. At least 4 years’ experience in Python, particularly Pandas, Dask, or Spark for large scale data analysis. At least 4 years’ experience with custom code-driven machine learning projects using packages such as scikit-learn, H2O, xgboost, PyTorch, JAX, etc. At least 4 years’ experience with SQL. Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity, or any other basis prohibited under applicable federal, state or local law.
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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 Retail Bank Data Science team has a relentless focus on innovation with a passion for improving customer experiences. For that, our team writes lots of custom Python and Bash code to develop and deploy advanced AI/ML techniques. We care very deeply about doing things the right way, automating, standardizing, and removing barriers to accelerate our speed to market. Whether it’s big or small data, our team’s ability to turn that data into insights and insights into great decisions hinges upon the code that makes it all work. This role will be the catalyst that drives data science culture towards adopting the best mix of internal and external platforms, tools, and practices to deliver model-based insights to our customers faster than ever before. Role Description
In this role, you will: Lead our code quality initiative, defining the vision and roadmap for the effort and carrying it out across the Retail Bank. Present the code quality initiative and progress to both leaders and peers within the Bank and across the company, while driving adoption throughout the team. Evaluate Machine Learning pipelines and Enterprise offerings to coach the team towards making the best development and deployment decisions for their use cases. Leverage expertise on platforms and software best practices to enable and improve data scientists’ code resiliency and performance. Develop and maintain software tools, playbooks, and documentation to further drive engineering practices that rapidly lead to high performance models. Curate or create training for software development best practices for data scientist mastery with tools used on model build and execution platforms. The Ideal Candidate is:
Technical.
You’re experienced with open source languages and how they’re used in artificial intelligence and machine learning. You understand what can and should be automated and standardized. A Teacher.
You know your way around a complex, collaborative code base, but you still remember what it was like writing your first script. A Self-Starter.
You will own the mission to define the structure that leads our team to eliminate errors and make work exceptionally transferable. Detail Oriented.
You appreciate elegance and efficiency in code. You believe the little things add up. Collaborative.
You work well with leaders and fellow data scientists to balance code quality needs with existing project timelines. Basic Qualifications:
Currently has, or is in the process of obtaining a Bachelor’s Degree plus 6 years of experience in data analytics, or currently has, or is in the process of obtaining a Master’s Degree plus 4 years of experience in data analytics, or currently has, or is in the process of obtaining PhD plus 1 year of experience in data analytics. At least 2 years’ experience in open source programming languages for large scale data analysis. At least 2 years’ experience with machine learning. At least 2 years’ experience with relational databases. Preferred Qualifications:
PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics. At least 1 year of experience working with AWS. At least 4 years’ experience in Python, particularly Pandas, Dask, or Spark for large scale data analysis. At least 4 years’ experience with custom code-driven machine learning projects using packages such as scikit-learn, H2O, xgboost, PyTorch, JAX, etc. At least 4 years’ experience with SQL. Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity, or any other basis prohibited under applicable federal, state or local law.
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