ML Benchmark Analyst: Data-Driven AI Evaluation

Hire Feed; Italia; A distancia; EUR 42.000 - 70.000; Jornada completa

Descripción de la vacante

Hire Feed is seeking a MLE Bench – Data Analyst to work full-time in Italy. You will analyze production-like datasets, develop metrics, and evaluate ML outputs to improve system performance. The role sits at the intersection of data analysis and ML, requiring diligence with real datasets and evaluation workflows.

The ideal candidate brings hands-on analytical rigor, strong Python skills, and solid SQL experience to contribute to benchmark-driven evaluation projects in a global tech environment.

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Python for data analysis

Statistics

Data analysis

ML evaluation

Herramientas

SQL

Descripción del empleo

Hire Feed is seeking a MLE Bench – Data Analyst to work full-time in Italy. You will analyze production-like datasets, develop metrics, and evaluate ML outputs to improve system performance. The role sits at the intersection of data analysis and ML, requiring diligence with real datasets and evaluation workflows.

The ideal candidate brings hands-on analytical rigor, strong Python skills, and solid SQL experience to contribute to benchmark-driven evaluation projects in a global tech environment.