Data Analytics Specialist

Summary

Join our amazing Validation Analytics Team as a Data Analytics Specialist! Focus on analyzing data and developing statistical methods to assess the quality of Image Recognition (IR) models' performance. Ensure high standards of testing AI models and assessing training data quality. Interface with internal teams (R&D, Labeling Team, Product & Engineering, Customer Success, and Sales Engineering). You'll be responsible for AI model validation, defining and adjusting analytical process requirements (accuracy, recall, precision), assessing data quality, establishing analysis standards, planning data cleaning, troubleshooting, performing root cause analysis, and monitoring quality metrics. Your work will significantly impact the quality of reporting for internal teams and customers, and you'll identify areas for system enhancements. This role is crucial for ensuring the Image Recognition Product delivers validated data in reporting outputs.

Requirements

• Have 2+ years of experience as a Data Analyst or similar role working with large data sets
• Share examples of your experience with data validation, statistical process control, root-cause analysis and what kinds of changes were implemented as a result to improve overall data quality
• Be experienced with data analysis tools like Python & MS Excel
• Be results-oriented – setting success metrics and measuring success against them to be sure you’re focused on the activities that make a measurable impact on external and internal customers’ lives
• Have strong analytical problem-solving skills, outstanding attention to detail, and a proven ability to meet established deadlines
• Be eager and able to help fix things and work calmly under pressure
• Have good organization and management of own work
• Collaborate with other teams across the organization to make the most effective use of data analytics
• Have English B2+ language skills

Responsibilities

• Analyze data and develop statistical methods to assess the quality of Image Recognition (IR) models' performance
• Ensure high standards of testing AI models and assessing their training data quality
• Interface with several internal teams including R&D, Labeling Team, Product & Engineering, Customer Success, and Sales Engineering
• Be responsible for validation of AI models
• State and adjust requirements of the analytical processes at all stages, such as accuracy, recall and precision
• Assess data quality
• Establish new and improve current analysis standards
• Prepare plans to clean data
• Troubleshoot and perform root cause analysis
• Plan corrective actions
• Monitor quality metrics
• Evaluate and identify where system enhancements are required to further improve validation methods