Manager, Data Science - Automation Excellence

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. The more time we can save through code that runs flawlessly and without intervention, the more time we have to deliver new insights. 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 effortrole 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. Modular code powering real-time predictive applications running in the cloud is kinda your thing.

• A Teacher. You know your way around a complex, collaborative code base, but you still remember what it was like writing your first script. You can articulate the why, the what, and the how of writing modular, unit-tested code so others can get on your level. The only thing better than writing your own masterful Python package is coaching someone else to leave their beloved Notebook behind and learn to do the same.

• A Self-Starter. You will own the mission to define the structure that leads our team to eliminate errors and make work exceptionally transferable through leveraging existing libraries/platforms as well as creating new modules where needed. Overall, you help others achieve a high level of coding maturity and be self-sufficient when putting work into production on a cloud infrastructure.

• Detail Oriented. Pre-commit hooks are as automatic as putting your seatbelt on when you get behind the wheel. You appreciate elegance and efficiency in code. You believe the little things add up and you can show the value of paying attention to the finer points of coding.

• Collaborative: You work well with leaders and fellow data scientists to balance code quality needs with existing project timelines. You are able to incorporate opinions from various stakeholders to come up with a plan that serves the most good.

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