- Design, build, and/or deliver machine learning models and components solving real-world business problems
- Collaborate with Product and Data Science teams
- Make ML infrastructure decisions involving model choice, data and feature selection, training, tuning, dimensionality, bias/variance, and validation
- Write and test application code, develop and validate ML models, and automate tests and deployment
- Collaborate on a cross-functional Agile team developing big data and ML applications
- Retrain, maintain, and monitor production models
- Build or leverage cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
- Construct optimized data pipelines feeding ML models
- Apply continuous integration and continuous deployment practices, test automation, and monitoring
- Manage code to reduce vulnerabilities and govern models from a risk perspective
- Apply Responsible and Explainable AI best practices
- Use Python, Scala, or Java
Requirements
- Bachelor's Degree
- At least 8 years of experience designing and building data-intensive solutions using distributed computing; internship experience does not apply
- At least 4 years of programming experience with Python, Scala, or Java
- At least 3 years of experience building, scaling, and optimizing ML systems
- At least 2 years of experience leading teams developing ML solutions
- Preferred: Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
- 4+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
- 3+ years of experience developing performant, resilient, and maintainable code
- 3+ years of experience with data gathering and preparation for ML models
- 3+ years of people management experience
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
- 3+ years of experience building production-ready data pipelines feeding ML models
- Ability to communicate complex technical concepts clearly to varied audiences
- Experience leveraging interactive AI tooling beyond basic code completion
- Capital One will consider sponsoring a new qualified applicant for employment authorization
Core Competencies
Demonstrates expertise in designing and building machine learning models and data-intensive solutions, with a strong focus on cloud-based architectures and continuous integration practices. Proven ability to lead teams and communicate complex technical concepts effectively.
Highest-signal resume keywords
- Machine Learning Model Development
- Python Programming
- Cloud-Based Architecture (AWS, Azure, Google Cloud)
- Data Pipeline Construction
- Team Leadership in ML Solutions
ATS Optimization Keywords
Hard Skills
- Machine Learning
- Data Preparation
- Model Training and Tuning
- Continuous Integration
- Continuous Deployment
- Distributed Computing
- ML Frameworks (scikit-learn, PyTorch, TensorFlow)
- Application Code Development
- Automated Testing
- Risk Management
Soft Skills
- Clear Communication
- Collaboration
- Team Management
Certifications & Qualifications
- Bachelor's Degree
- Master's or Doctoral Degree (Preferred)
Industry Keywords
- Responsible AI
- Explainable AI
- ML Infrastructure
- Production Models
- Data-Intensive Solutions
Tools & Technologies
- Python
- Scala
- Java
- Big Data Technologies
- Agile Methodologies