Machine Learning Engineer

About the Company

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition.

Responsibilities

• 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 process, documentation, and coding standards
• Participate in development of database structures that fit into the overall architecture of Swish systems

Requirements

• 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
• Proven background in quantitative analytics, trading, or engineering
• Demonstrated experience developing data science modeling systems and infrastructure at scale
• Experience with Python and modern machine learning frameworks
• Proficient in SQL and experience with MySQL

Preferred Qualifications

• Background and/or interest in Rust