MLE Bench – Data Analyst
Overview
Turing runs benchmark-driven evaluation projects for frontier AI labs, and this role focuses on data analysis for MLE Bench. You will inspect real-world machine learning outputs, define and validate metrics, and write reproducible Python and SQL analysis scripts. The 3-month contractor engagement requires at least 20 hours per week and a 4-hour daily overlap with PST.
What You'll Do7
- 1Analyze structured and unstructured datasets generated by ML training, inference, and evaluation pipelines to uncover patterns and anomalies.
- 2Define and compute metrics that measure model performance, then validate those metrics for accuracy and relevance.
- 3Investigate data distributions, model outputs, failure modes, and edge cases tied to benchmark tasks.
- 4Write and run Python and SQL code to produce reports and support the evaluation workflow.
- 5Check datasets and experimental results for quality, consistency, and correctness.
- 6Document your analytical methods and create reproducible workflows that others can follow.
- 7Collaborate with ML engineers and researchers to design realistic evaluation scenarios for MLE Bench.
Requirements8
- 1At least 3 years of experience as a data analyst or analytics-focused engineer.
- 2Strong Python skills for data analysis, including writing clean and well-documented code.
- 3Solid experience with SQL and working with relational datasets.
- 4Hands-on experience analyzing ML outputs and evaluation metrics.
- 5A firm grasp of statistics and analytical reasoning.
- 6Ability to work with large, complex datasets and draw reliable conclusions.
- 7Excellent written and spoken English for clear communication.
- 8Comfort with documenting analysis and creating reproducible processes.
Who Should Apply
The ideal candidate has several years of hands-on data analysis experience, deep familiarity with Python and SQL, and a genuine interest in how machine learning models are evaluated. This role is less suitable for analysts who prefer static dashboards over probing model behavior or who lack comfort with statistical reasoning. Candidates often get rejected when they struggle with the live coding interview, especially on SQL queries or Python data manipulation. Another common miss is failing to show a clear work schedule that accommodates the required 4-hour overlap with PST.
Location
Required Skills
Application Tip
Before you apply, run through a timed Python and SQL coding exercise that mimics a data analysis task, such as computing a metric from a sample dataset. Be ready to explain how you validate metrics and handle messy data, and mention any past work with ML evaluation in your application.
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1Application received
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2ATS + eligibility screening
We check your profile against the role’s skills, seniority, and requirements.
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