Role Overview:
We are hiring for one of our clients, seeking a MLE Bench – Data Analyst to work on a Full-time basis. This role involves hands-on analytical work with production-like datasets, metrics, and ML outputs to help evaluate, diagnose, and improve the performance of advanced AI systems. The ideal candidate is comfortable working at the intersection of data analysis and machine learning, with strong analytical rigor and the ability to work with real datasets and ML evaluation workflows.
Key Responsibilities:
• Analyze structured and unstructured datasets generated from ML training, inference, and evaluation pipelines.
• Define, compute, and validate metrics used to evaluate model performance and behavior.
• Investigate data distributions, model outputs, failure modes, and edge cases relevant to benchmark tasks.
• Write and run Python and SQL code to analyze data, create reports, and support evaluation workflows.
• Validate data quality, consistency, and correctness across datasets and experiments.
Required Skills & Qualifications:
• Minimum 3+ years of experience as a data analyst or analytics-focused engineer.
• Strong proficiency in Python for data analysis.
• Solid experience with SQL and relational datasets.
• Experience analyzing ML outputs and evaluation metrics.
• Strong understanding of statistics and analytical reasoning.
More About the Opportunity:
This role offers a unique opportunity to work with a global leader in the Software Development | Technology, Information and Internet | Data Infrastructure and Analytics industry, contributing to benchmark-driven evaluation projects focused on real-world machine learning systems. The position involves collaboration with ML engineers and researchers to design challenging evaluation scenarios.
Equal Opportunity Employer:
We hire based on skills and expertise. All qualified candidates are welcome regardless of background, experience, or prior employment history. Applications are reviewed solely on demonstrated technical ability and qualifications.
Apply Now!
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