Manager, Data Science- Model Risk

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

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 to explore small as well as big data problems.

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 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.

 On any given day you will find yourself:

• Building benchmark and challenger models to stress test critical modeling decisions
• Developing and defending techniques for evaluating model performance
• Reading up on a new modeling algorithm that the business is starting to use
• Using modern computing tools (e.g. , Spark, H2O, AWS) to perform an analysis of bias in a model’s training data
• Evaluating deployment architecture and code quality for a new machine learning system
• Writing blog posts or presentations to introduce our modeling community to emerging best practices
• Developing software tools that support well-managed model development and maintenance

The ideal candidate is:

• A strong communicator. You are able to defend your own reproducible work in technical reports, as well as respectfully explain complex ideas and project influence to a diverse, collaborative audience. 
• A constructive skeptic. Able to internalize the wisdom of the community, but also can see where changing facts require new ideas and is not afraid to say so.
• A data wrangler. You know how to programmatically extract data from a database and an API, bring it through a transformation or two, and model it into human-readable form (ROC curve, map, d3 visualization, Tableau, etc.).
• A results-focused modeler. You understand and love Statistics and Machine Learning theory, but you also know that a model’s value emerges from a complex interaction of theory, people, data, and processes.

Basic Qualifications:

• Bachelor’s Degree plus 6 years of experience in data analytics, or Master’s Degree plus 4 years of experience in data analytics, or PhD plus 1 year of experience in data analytics
• At least 2 years’ experience in open source programming languages for small and 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 or R for small and large scale data analysis
• At least 4 years’ experience with machine learning

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.