Remote Statistician for AI Training Data

RemoteJobsOne; Madrid; A distancia; EUR 73.000 - 145.000; A tiempo parcial

Descripción de la vacante

RemoteJobsOne is seeking a Statistician for a fully remote contractor role open to candidates based in Spain. You will clean and analyze real-world datasets, create visualizations, and document methods for non-technical audiences. Strong Python or R skills and experience handling messy data are preferred. Collaboration in a remote setting is essential.

The role focuses on contributing expert knowledge to AI model training, with emphasis on clear communication and rigorous data quality practices.

Formación

• MS or PhD in a quantitative field completed.
• Experience cleaning messy datasets.
• Proficiency in Python or R for analysis and visualization.
• Ability to explain methods to non-technical audiences.
• Experience creating data visualizations and reports.

Responsabilidades

• Clean, preprocess, and structure datasets using statistical software.
• Apply descriptive and inferential analyses to real-world data.
• Develop clear data visualizations to illustrate findings.
• Annotate and enrich datasets for AI model training.
• Draft concise written summaries of methods and results.
• Collaborate asynchronously with stakeholders to clarify requirements.
• Identify data quality issues and document remediation steps.

Conocimientos

Data storytelling

Remote collaboration

Statistical reasoning

Educación

MS or PhD in Statistics or related quantitative field

Herramientas

Python

R

SAS

Stata

Descripción del empleo

RemoteJobsOne is seeking a Statistician for a fully remote contractor role open to candidates based in Spain. You will clean and analyze real-world datasets, create visualizations, and document methods for non-technical audiences. Strong Python or R skills and experience handling messy data are preferred. Collaboration in a remote setting is essential.

The role focuses on contributing expert knowledge to AI model training, with emphasis on clear communication and rigorous data quality practices.