McLean 1 (19050), United States of America, McLean, Virginia
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 on a mission to help 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.
Are you a high performing software engineer and/or data scientist that has experience leading, mentoring and ideally managing a team of data scientists and engineers? Are you looking to apply the latest machine learning research in production implementations? Do you enjoy collaborating with your engineering team and your business partners to find creative ways to use machine learning to drive business objectives? You could be a great match for a Senior Manager role at Capital One's Center for Machine Learning (C4ML).
As a Senior Manager in C4ML, you will manage teams that build fast data and machine learning solutions to create and improve some of the most interesting use cases in the financial services industry. You will work alongside the Directors and Vice President of C4ML to solve critical problems for the organization. Capital One maintains a full stack of technology solutions including streaming big data, state of the art machine learning, micro-service architecture, distributed computation engines, and intuitive visualizations in the cloud. To manage this, we are working with several cutting-edge technologies and are actively developing and contributing to the open source community. We are highly technical with strong backgrounds in our fields to support use cases ranging from cyber threat prevention to sophisticated NLP understanding in an always on 24/7 service architecture. We have the highest executive support for acting as a catalyst of machine learning across Capital One, driving the innovative use of machine learning in the most important Capital One problems.
What you will bring to the role:
- Excellent communication skills
- Leadership skills, mentorship, and management of team members
- Demonstrated ability to build full-stack systems architected for speed and distributed computing.
- Demonstrated ability to quickly learn new tools and paradigms to deploy cutting-edge solutions.
- Experience teaching junior engineers.
- Adept at simultaneously working on multiple projects, meeting deadlines, and managing expectations.
What you will do in the role:
- Work with subject matter experts in the business to design and deliver production applications that solve their problems through machine learning.
- Guide data scientists and full-stack engineers on your team to build sustainable and high-quality software solutions using the latest advancements in data science, data engineer, and cloud-engineering.
- Effectively leverage the talent on your team to agilely deliver incremental value
- Actively participate in building our organization and growing our talent
- Bachelor's Degree or Military Experience
- At least 5 years of experience designing and building full stack solutions
- At least 5 years of experience working with Python, Scala or Java
- At least 2 years leading teams in code development and balancing feature requests with feasibility constraints
- At least 2 years managing people & teams
- Master's Degree or PhD
- At least 2 years working on consulting or client service engagements
- At least 2 years building applications that use statistical or machine learning methods
- At least 2 years utilizing distributed computing or cloud-based solutions
- A history of publications and conference attendance
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