bpx is looking to implement and maintain data science models in their machine learning studio, bridging the gap between data science and computational requirements to achieve business outcomes.
Requirements
- Strong programming skills: Python and Cloud Implementation Scripting
- Experience with big data, real-time streaming data technologies, and cluster computing environments.
- Knowledge and exposure to cloud technologies, especially AWS.
Responsibilities
- Implement data science algorithms in bpx’s ML Studio SageMaker).
- Create systems and processes to monitor performance of ML algorithms in production.
- Serve as the subject matter expert of ML Operations and guides data scientists in the practical implications of model design
- Collaborate with the data engineering team to build and maintain data pipelines from systems like Snowflake and OSI Pi
- Partner with bpx Architecture team to ensure endpoints, compute, and network considerations are built into solutions
- Takes initiative and stays up to date with the latest data science trends, techniques, and best practices, determining how to incorporate the most suitable practices in the department.
- Design systems to balance cost and performance to meet business outcomes
Other
- The role will have the ability to guide bpx’s data science journey in a nascent technology stack.
- The ML Engineer will have significant freedom and latitude to suggest and implement solutions.
- Work as part of geographically dispersed team, effectively communicating prioritized business needs and prioritized project statuses.
- A Bachelor’s degree (Master’s preferred) in Statistics, Mathematics, Computer Science, or any other related quantitative field.
- 7+ years in data science or related field, 3+ years of hands-on experience in machine learning operations.
- Proven track record of implementing and scaling models in an operations or customers focused company
- Must be legally authorized to work in the US without sponsorship.
