Senior Data Scientist (Machine Learning/Control Systems)

United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Entertainment, Biotechnology, Theme ParksIndustries

Requirements

Candidates must possess a Bachelor's degree in Mathematics, Statistics, Data Science, or a related field, or have equivalent work experience. A minimum of 5 years of experience in data analytics and modeling using TensorFlow or PyTorch is required, along with at least 3 years of hands-on experience deploying ML models into production environments. Proficiency in Python, deep learning algorithms (e.g., autoencoders, transducers), version control systems like GitHub or GitLab, and familiarity with data visualization and monitoring tools (e.g., Splunk, Plotly, Tableau) are essential. Experience with Atlassian tools such as Jira and Confluence is also necessary. Preferred qualifications include knowledge of SDLC and QA processes, experience with anomaly detection or predictive maintenance, proficiency with Docker and Kubernetes, experience setting up CI/CD pipelines and using workflow orchestration tools like Airflow, and an understanding of object-oriented software design patterns. A Master's degree in a related discipline is preferred.

Responsibilities

The Senior Data Scientist will design and implement robust, end-to-end machine learning pipelines to drive predictive maintenance across attractions. They will collaborate with data scientists and ML engineers to optimize model performance and training workflows, and deploy and monitor models in production using MLOps tools like Airflow, Kubeflow, Docker, and Kubernetes on cloud platforms. Responsibilities include analyzing system performance, troubleshooting deployment issues, partnering with IT and DevOps for integration, monitoring model health against benchmarks and SLAs, managing compute and storage resources efficiently, and implementing automated testing frameworks. The role also involves mentoring team members on MLOps best practices and staying current with advancements in data science, machine learning, and MLOps to apply them innovatively.