Machine Learning Engineer
Swish Analytics•San Francisco, CA
•Remote
About The Position
Swish Analytics is hiring an experienced Machine Learning Engineer for its Data Science team. The role focuses on building machine learning and statistical modeling frameworks from scratch and improving the modeling process across data validation, visualization, data stores and structures, feature engineering, model training and evaluation, and deployment. The engineer will develop custom solutions to complex sports data challenges across multiple leagues and help improve Swish products. The position is 100% remote.
Requirements
- Master’s degree in Computer Science, Applied Mathematics, Data Science, Computational Physics or Chemistry, or a related technical subject area.
- At least 5 years of demonstrated experience developing and delivering clean, efficient production code to serve business needs.
- A proven background in quantitative analytics, trading, or engineering.
- Demonstrated experience developing data science modeling systems and infrastructure at scale.
- Experience with Python and exposure to modern machine learning frameworks.
- Proficiency in SQL; experience with MySQL.
- Teamwork and collaboration skills, including sharing knowledge and providing feedback.
- Strong communication skills for discussing technical concepts with technical and non-technical colleagues.
Nice To Haves
- Background in or interest in Rust.
Responsibilities
- Design, prototype, implement, evaluate, and optimize systems that generate accurate, low-latency sports datasets and predictions.
- Evaluate internal modeling frameworks and tools to improve data scientists’ modeling workflows.
- Build, test, deploy, and maintain production systems and production-grade components.
- Work with DevOps and Data Engineering teams to implement, optimize, 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 software development best practices, including deployment processes, documentation, and coding standards.
- Apply large-scale data processing techniques to develop scalable sports betting products.
- Participate in developing database structures that fit the overall architecture of Swish systems.
Benefits
- Bonus potential.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
