1750 Tysons (12023), United States of America, McLean, Virginia
Data Science Manager - Python Standards Implementation
At Capital One, we're building a leading information-based technology company. Still founder-led by Chairman and Chief Executive Officer Richard Fairbank, Capital One is focused on helping our customers succeed by bringing ingenuity, simplicity, and humanity to banking. We measure our efforts by the success our customers enjoy and the advocacy they exhibit. We are succeeding because they are succeeding.
Guided by our shared values, we thrive in an environment where collaboration and openness are valued. We believe that innovation is powered by perspective and that teamwork and respect for each other lead to superior results. We elevate each other and obsess about doing the right thing. Our associates serve with humility and a deep respect for their responsibility in helping our customers achieve their goals and realize their dreams. Together, we are on a quest to change banking for good.
As a Manager Data Scientist in the Retail and Direct Bank, you'll be part of a high performing team that is working to define the next generation of banking. The Bank Data Science team has a relentless focus on the craft of modeling, coding, and innovation with a target towards continually improving customer experience and delivering value to the business. Using the latest in machine learning and distributed computing technologies, you will be building the next generation of data products to enable automation and aim for the right decision at the right time for in-the-moment action.
T his role is centered around the application of Python and its flexibility across imperative, object-oriented, and functional programming styles. It includes building reusable assets in a Pythonic environment and embodies core principles of The Zen of Python. While an early focus of the role will be on designing and building the model development and execution patterns of the future, there will remain a consistent and ultimately primary intent to establish, educate, and evangelize the best practices required of data scientists to successfully use these platforms with robust and resilient code. The role requires a willingness to teach these principles to other members on the team.
In this role you will:
- Own in-house developed tools & libraries in support of statistical and machine learning model building and deployment to various execution platforms including:
- New tool and platform discovery and investigation
- Technical documentation in support of playbook(s) standards, FAQs
- Tool adoption, modification, and development to standardize, automate and inner-source best practices for data source access, model development, model promotion to production and model monitoring
- Developing or curating training for software development best practices for data scientist mastery on model build and execution platforms
- Developing code and repo quality standards and train data scientists to adopt and adhere to these standards with structured peer code reviews
- Host office hours or other avenues to assist data scientists in need of assistance on model build and execution platforms and tools
- Engage with the data science community to solicit feedback and lead virtual or in-person training sessions
- Develop and maintain up to date playbooks for the tools and development practices
- Bachelor's Degree plus 6 years of experience in data analytics, or Master's Degree plus 4 years in data analytics, or PhD plus 1 year of experience in data analytics
- At least 2 years of experience in open source programming languages for large scale data analysis
- At least 2 years of experience with machine learning
- At least 2 years of experience with relational databases
- Bachelor's Degree or Master's Degree in Computer Science, Computer Engineering, Statistics, Math plus 3 years of experience in data analytics
- At least 1 year of experience and proficiency in working with AWS (S3, EMR, EC2, IAM, Lambda)
- At least 2 years of experience with containerization (Docker)
- At least 4 years of experience working in Python
- At least 4 years of experience with PyData software stacks (pandas, numpy, scipy, sklearn, statsmodels)
- At least 4 years of experience with machine learning
- At least 4 years of experience with SQL
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
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