Amateur Data Technician

Department : Amateur Scouting

Reports to : Senior Analyst, Baseball R&D

Location : St. Petersburg, FL

Overview The Amateur Data Technician will sit at the intersection of Amateur Scouting and Data Engineering, ensuring the completeness, accuracy, and accessibility of college/high school data to maximize its impact on the player evaluation process for the annual MLB Draft. This person will develop a thorough understanding of available data sources and the full data pipeline, from ingestion into the database through integration into the amateur model. The technician will work alongside analysts and engineers to support and maintain data pipelines, ensure reliable and complete data delivery, develop quality-control processes, identify and reduce query bottlenecks, and explore opportunities to incorporate underutilized information. The technician will also gain an understanding of the amateur model, assist with its day-to-day operation and maintenance, and support various ad hoc statistical and draft-related tasks.

Responsibilities (65%) Amateur Data Oversight

• Develop and maintain processes to track data delivery and ensure completeness
• Implement quality checks to identify recurring issues, including erroneous or duplicate data
• Develop a working understanding of existing data pipeline frameworks and historical considerations
• Partner with Amateur Scouting staff to improve the accuracy and efficiency of data mapping processes
• Optimize existing queries and develop new ones to reduce data-access bottlenecks
• Maintain a thorough understanding of amateur data sources and their characteristics

(35%) Amateur Analytical Support

• Understand the amateur model framework and impact of underlying data sources
• Assist with the execution and maintenance of the amateur model pipeline
• Conduct ad hoc analytical explorations and support other draft-related tasks

Qualifications

• Applied experience with SQL and relational databases
• Fluency in R
• Experience working with large or complex datasets and troubleshooting data-quality issues
• Working knowledge of statistical modeling and machine learning
• Experience in amateur baseball or with TrackMan data a plus
• Fluency in Python a plus

All offers are contingent on a satisfactory background check. We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.