- With an enterprise-wide view of data and machine learning initiatives, design scalable and repeatable solutions to machine learning and AI projects that impact and influence the company s analytics strategy, including designing platforms and APIs for models
- Design and steer the policies and patterns for how other teams interface with machine learning models.
- Strategically understand and devise solutions for how to incorporate new machine learning + AI solutions into our tech stack across a wide variety of use cases
- Establish best practices across machine learning engineering team to enable and accelerate multiple critical support use cases
- Lead a team of experts to continuously monitor deployed models, build scalable solutions to identify data issues such as detecting data drift and model drift of new and existing machine learning processes
- Keep up with ML Ops technology trends and vendors to understand new capabilities and continuously innovate
- Subject matter expert for building processes that ensure reproducibility of models by supporting versioning of data and code, CI/CD, containerization (Kubernetes)
Education/Work Experience:
- Masters degree in Computer Science, Information Systems, Machine Learning, Data Science, or a related field.
- Proficiency in programming languages such as Python, and experience with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- 12 - 15 years relevant experience working with production machine learning pipelines.
PREFERRED:
Master s degree in a STEM field with 5+ years of relevant work experience
Skill Requirements:
Must demonstrate the ability to manage stakeholders, the capacity to think strategically, and the aptitude to coach, mentor, and manage highly technical FTEs.
