Design, prototype, implement, evaluate, 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.
Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.
Use extensive experience to build, test, debug, and deploy production-grade components.
Participate in development of database structures that fit into the overall architecture of Swish systems.

Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area
5+ years of demonstrated experience developing and delivering clean and efficient production code to serve business needs
A proven background in quantitative analytics, trading, or engineering is required for this position
Demonstrated experience developing data science modeling systems and infrastructure at scale
Experience with Python and exposure to modern machine learning frameworks
Proficient in SQL; experience with MySQL
Background and/or interest in Rust preferred
Affinity for teamwork and collaboration with others to solve problems, share knowledge, and provide feedback
Strong communication skills when discussing technical concepts with technical and non-technical colleagues.

Machine Learning Engineer

Key skills