Remote Statistician for AI Data Training & Insights

RemoteJobsOne; Singapore; Remote; SGD 83,000 - 165,000; Part time

Job summary

RemoteJobsOne is seeking a Statistician for a fully remote contractor role open to candidates based in Singapore. You will clean and structure complex datasets, perform descriptive and inferential statistics, and develop visualizations to support AI model training.

You’ll work asynchronously with stakeholders, documenting methods and results for a non-technical audience and contributing to data quality improvements in real-world projects.

Qualifications

• Advanced degree (MS or PhD) in statistics, data science, mathematics, biostatistics, or related field.
• Expertise in cleaning and preparing complex datasets with R, Python, SAS, or Stata.
• Proficiency in basic descriptive and inferential statistics, incl. hypothesis testing and regression.

Responsibilities

• Clean, preprocess, and structure complex and messy datasets using advanced statistical software (R, Python, SAS, or Stata).
• Apply and document descriptive and inferential statistics to uncover trends in real-world data.
• Develop clear data visualizations to illustrate findings and support model development.
• Contribute expertise in dataset annotation, labeling, or enrichment for AI model training data.
• Draft concise summaries of methods, analyses, and results for non-technical audiences.
• Collaborate asynchronously with stakeholders to clarify requirements and improve deliverables.
• Identify data quality issues and document solutions for handling dirty data.

Skills

Statistics

Data Cleaning

Python

R

Data Visualization

Communication

Education

MS or PhD in Statistics / Data Science

Tools

R

Python

SAS

Stata

Job description

RemoteJobsOne is seeking a Statistician for a fully remote contractor role open to candidates based in Singapore. You will clean and structure complex datasets, perform descriptive and inferential statistics, and develop visualizations to support AI model training.

You’ll work asynchronously with stakeholders, documenting methods and results for a non-technical audience and contributing to data quality improvements in real-world projects.