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As a Product Manager I, you will define innovative sports betting products, collaborate with various teams including Data Science and Engineering, ensure smooth execution of product strategies, and align on content strategies with the trading team while evaluating in-house models for profitability and availability.
As a Data Science Engineer, you will develop and integrate statistical and machine learning models into applications for a sportsbook platform, ensuring model accuracy and collaborating with various teams to enhance sports modeling capabilities.
As a Manager of Data Science Engineering, you will manage a team of sports modellers, oversee the design and development of sports models and applications, collaborate with business leads, contribute to DS project architecture, and mentor engineers to ensure high-quality output.
Lead Data Science Engineer role at a sports and entertainment technology company involved in developing cutting-edge products and shaping the future of responsible gaming. Responsibilities include implementing DS applications, creating statistical and machine learning models, data engineering, writing production quality code, collaborating with various teams, and coaching junior data scientists. Requires expertise in Python, database technologies, ML automation, AWS, DevOps principles, and Kubernetes. Strong communication and organizational skills are essential.
As a Software Engineering Manager, you will oversee an Engineering team, focusing on developing automation and efficient solutions for the Sportsbook vertical, mentoring team members, and ensuring high-quality technical delivery. You will manage project lifecycles while collaborating with business leaders on application functionality.
As a Senior Data Science Engineer, you will develop and integrate statistical and machine learning models into applications, enhance football models, write production quality code, utilize MLOps for training, and support junior data scientists. You will collaborate closely with various teams and ensure data flows are accurate and models function within the business context.
As a Machine Learning Engineer, you'll integrate statistical and machine learning models into production, write production quality code, utilize MLOps for ML workloads, and ensure model accuracy. Collaboration with product teams is vital to move projects from ideation to deployment.
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