The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. They can assist with optimizing the different aspects of the modeling process (Data Validation, Data Visualization, Data Stores & Structures, Feature Engineering, Model Training & Evaluation, Deployments) and improving a variety of Swish products. They will know when to "roll your own" and when to outsource a particular step in the modeling process. They will engineer custom solutions to solve complex data-related sports challenges across multiple leagues.
This position is 100% remote Responsibilities:- 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.
- Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.
- Participate in development of database structures that fit into the overall architecture of Swish systems
Qualifications:- 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
Base salary: starting at $160,000 base plus bonus potential
Department Engineering & Infrastructure Role Data Science Infrastructure Locations San Francisco, CA - Remote Remote status Fully Remote
