Karkidi
Machine Learning Engineer
Karkidi, Boston, Massachusetts, us, 02298
The Crown Is YoursOur Data Science team is composed of algorithm experts and data science technologists who leverage the power of data to deliver transformative experiences for our users and drive continued innovation. As a Machine Learning Engineer, you will be a creative thinker and utilize data, machine learning, and software development skills to craft high-impact solutions that transform our business.What you'll do as a Machine Learning Engineer
Integrate statistical and machine learning models into production applications.Write production quality code to deploy and run models in a sportsbook platform.Utilize our MLOps platform to productionize ML workloads.Create automatic tests to ensure model accuracy.Collaborate closely with product, developers, and delivery leads to move projects from ideation to development and deployment.Test that data flows work as expected and that models are well integrated in a larger business context.What You'll bring
Experience using Python, and its application to data science and data engineering.Experience working in a cloud environment.Experience with Docker and running containerized services (e.g., Kubernetes, Docker Compose).Experience of using observability tooling for production monitoring and alerting, such as DataDog, Grafana, Kibana.An understanding of event-driven messaging systems (e.g., Kafka, RabbitMQ), ideally with real-world experience.Experience with object-oriented programming.Knowledge of infrastructure as code (e.g., Terraform) is also beneficial.Understanding of data science and statistical modeling principles will be considered an asset.Bachelor’s degree in Statistics, Data Science, Mathematics, Computer Science, or a software engineering related field.
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Integrate statistical and machine learning models into production applications.Write production quality code to deploy and run models in a sportsbook platform.Utilize our MLOps platform to productionize ML workloads.Create automatic tests to ensure model accuracy.Collaborate closely with product, developers, and delivery leads to move projects from ideation to development and deployment.Test that data flows work as expected and that models are well integrated in a larger business context.What You'll bring
Experience using Python, and its application to data science and data engineering.Experience working in a cloud environment.Experience with Docker and running containerized services (e.g., Kubernetes, Docker Compose).Experience of using observability tooling for production monitoring and alerting, such as DataDog, Grafana, Kibana.An understanding of event-driven messaging systems (e.g., Kafka, RabbitMQ), ideally with real-world experience.Experience with object-oriented programming.Knowledge of infrastructure as code (e.g., Terraform) is also beneficial.Understanding of data science and statistical modeling principles will be considered an asset.Bachelor’s degree in Statistics, Data Science, Mathematics, Computer Science, or a software engineering related field.
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