Product Engineer (MLOps)

Job Description

Swish Analytics is seeking a skilled Product Engineer (MLOps) to join their Data Science team. This role is 100% remote and involves building machine learning and statistical modeling frameworks from scratch, optimizing various aspects of the modeling process, and engineering custom solutions for complex data-related sports challenges across multiple leagues. The successful candidate will:

• Design, prototype, implement, evaluate, and optimize systems to generate sports datasets and predictions with high accuracy and low latency.
• Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow.
• Build, test, deploy, and maintain production systems.
• Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages.
• Support maintenance and optimization of cloud-native EDW and ETL solutions.
• Maintain and promote best practices for software development, including deployment processes, documentation, and coding standards.
• Apply large-scale data processing techniques to develop scalable and innovative sports betting products.
• Participate in the development of database structures that fit into the overall architecture of Swish systems.

Requirements include:

• A Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area
• 5+ years of experience developing and delivering clean and efficient production code
• Experience developing data science modeling systems and infrastructure at scale
• Proficiency in Python and exposure to modern machine learning frameworks
• Proficiency in SQL; experience with MySQL
• Background and interest in Rust (preferred)
• Strong communication skills

Swish Analytics offers:

• A chance to work on cutting-edge sports analytics products.
• A fully remote work environment.
• Opportunity to work with modern technologies like Kubernetes, CI/CD, and cloud-native EDW/ETL Solutions.