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Swish Analytics
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The Soccer Data Scientist will develop and improve machine learning and statistical models for sports betting products. Key responsibilities include creating feature sets, testing models, and collaborating with engineering teams for deployment, while adhering to software engineering best practices.
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The DevOps Engineer will develop and maintain Kubernetes clusters for ML workloads, collaborate with Data Science and Engineering teams, implement CI/CD and automation tools, ensure system reliability, and manage incidents. The role also requires on-call work as needed.
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The Staff Accountant will assist the accounting team with daily, monthly, and annual activities, including preparing journal entries, generating and applying cash to AR invoices, recording payroll, and preparing reconciliations. This role also supports consolidation of financial statements and establishes accounting policies and procedures.
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The Product Engineer will enhance existing models, establish KPIs, improve the Rust codebase, manage data accuracy through Python, develop production-grade components, and support innovative sports betting products while adhering to software engineering best practices.
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The Customer Success Associate at Swish Analytics will manage client inquiries and ensure effective communication of products and services. The role involves identifying customer challenges and collaborating with internal teams to provide solutions, requiring strong communication skills and a background in customer experience.
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The Product Engineer (MLOps) will design and implement systems for generating sports datasets and predictions, optimize modeling frameworks, and collaborate with DevOps and Data Engineering teams. They will maintain production systems and execute large-scale data processing techniques for sports betting products.
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The Staff Software Engineer will be the technical lead for critical backend applications, responsible for optimizing existing applications, designing new services, leading code reviews, and ensuring adherence to coding standards, all while working with distributed data and engaging with various stakeholders.
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The Senior Trading Analyst will manage client risk in a remote role, ensuring high margins through positive expected value decisions. Responsibilities include overseeing depth chart accuracy, researching verified news sources regarding betting impacts, and analyzing trends to inform trading actions using a scientific approach.
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The DevOps Engineer will develop and manage Kubernetes clusters for ML workloads, collaborate with Data Science and Engineering teams, optimize infrastructure, implement best practices for software deployment, monitor systems, and provide on-call support as needed.
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The Soccer Data Scientist will develop and enhance machine learning and statistical models tailored to sports betting products, particularly soccer. Responsibilities include collaborating on model development stages, improving model performance through experimentation, analyzing results for performance assessment, and documenting work. Strong communication skills and adherence to best engineering practices are essential.
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The Technical Project Manager will lead and coordinate data science and data engineering initiatives, ensuring alignment with business objectives, managing project resources, and communicating progress with stakeholders. This role requires collaboration with cross-functional teams to deliver on strategic goals in a dynamic environment.
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The NHL Data Scientist will develop and improve machine learning and statistical models for sports betting products. Responsibilities include model development, performance analysis, collaboration with data engineering, and documentation of modeling work.
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The NFL Data Scientist role involves developing and improving machine learning and statistical models for sports betting products, collaborating across teams to deliver effective solutions, and improving model performance through experimentation and analysis. Strong documentation and communication skills are essential, along with expertise in data analytics and related technologies.
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The Data Scientist will develop and improve machine learning and statistical models for Swish's sports betting products, analyze model performance, and work with cross-functional teams for deploying new models while adhering to software engineering practices.
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The Tennis Data Scientist will develop and enhance machine learning and statistical models for sports betting products. They will work through all model development phases, improve model performance, analyze results, and document their work for stakeholders. The role emphasizes collaboration with engineering and product teams to deploy new models effectively.