Machine Learning Engineer

November 19

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Logo of Swish Analytics

Swish Analytics

Gambling • Gaming • Sports

Swish Analytics is a company that specializes in machine learning for US sports betting and fantasy sports. They deliver accurate, algorithm-driven predictions and tools to help improve betting strategies across major sports leagues such as the NBA, NFL, MLB, and NHL. Swish Analytics offers tools like lineup optimizers for daily fantasy sports and is an Authorized MLB Data Distributor to U. S. sports betting operators. Their team consists of data scientists, machine-learning engineers, and developers dedicated to providing powerful analytics and innovative solutions for the sports betting and gaming industry.

11 - 50 employees

🎲 Gambling

🎮 Gaming

⚽ Sports

💰 $6.9M Series B on 2019-05

📋 Description

• 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

🎯 Requirements

• 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.

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