- Design, prototype, implement, evaluate, optimize and monitor machine learning algorithms and related software 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
- Maintain and promote best practices for software development, including deployment process, documentation, and coding standards
- Contribute to technical and product discussions, and share knowledge and ideas with colleagues across the company
- Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area; PhD degree preferred
- Demonstrated experience developing data science modeling systems and infrastructure that scales
- Proficient in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods
- 5+ years of demonstrated experience developing and delivering effective machine learning models to serve business needs
- Experience with Python and modern machine learning frameworks
- 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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Machine Learning Engineer - San Francisco, CA, United States - Swish Analytics
Description
Company Overview
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. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and enterprise clients. 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: