Machine Learning Operations Engineer, Baseball Systems
4 weeks ago
The Detroit Tigers are seeking a Machine Learning Operations Engineer, Baseball Systems to design, manage, and automate data processes related to quantitative models in support of Baseball Operations initiatives. This role will report to the Director, Baseball Systems.
Key Responsibilities:
- Analyze large datasets and determine the optimal methods for data preparation.
- Collaborate with data scientists and other data engineers to build effective data pipelines.
- Design, develop, and maintain efficient and extensible model deployment frameworks.
- Manage, maintain, scale, and improve machine learning models in production environments.
- Create and maintain effective access methods and patterns for production models.
- Create support systems and dashboards to effectively communicate data insights to our data users.
- Collaborate with the larger Systems team to plan and build data infrastructure.
- Assist in other data engineering tasks as needed.
Minimum Knowledge, Skills, and Abilities:
- 2+ years of relevant work experience in data and/or ML engineering.
- Academic, professional, and/or research experience with applied mathematical and predictive modeling.
- Proficiency in back-end development, particularly with Python and R.
- Experience querying SQL data warehouses built for data science and analytics.
- Familiarity with cloud computing, cloud storage, and cloud services - preferably Azure.
- Familiarity with cloud-based data lakes, data warehouses, and related tooling.
Preferred Knowledge, Skills & Abilities:
- Familiarity with Airflow.
- Experience using Apache Spark (Databricks on Azure preferred).
- Strong SQL skills (T-SQL preferred).
- Experience working with containers and container deployment.
Working Conditions:
- Office environment.
- The location may be based in Detroit or fully remote.
- Occasional evening, weekend, and holiday hours are required.
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