Remote Data Scientist - AI Model Evaluation & Experimentation

Synthires; California; Remote; USD 207,000 - 276,000; Part time

Benefits offered by this job

Fully remote work

Independent contractor engagement

Job summary

Synthires is seeking a Data Scientist for a remote, contractor engagement to support an AI research project. You will evaluate model performance across statistics, machine learning, experimentation, and quantitative reasoning, contributing to accuracy and analytical capabilities.

Your duties include creating prompts and datasets, identifying methodological flaws, and delivering structured feedback to guide improvements.

Qualifications

• At least 1 year of experience at a leading company in technology, finance, research, or a related field, with some experience within the past 3 years.
• Strong understanding of Python, statistics, machine learning, and data analysis.
• Experience with Pandas, data modeling, and experimentation.
• Strong analytical and quantitative reasoning skills.
• Ability to communicate technical and statistical concepts clearly in writing.
• Based in an English-speaking country.

Responsibilities

• Evaluate AI-generated outputs involving data science, statistics, machine learning, and quantitative problems.
• Create challenging prompts, datasets, and reference solutions.
• Identify flawed methodologies, statistical errors, and weaknesses in quantitative reasoning.
• Assess data analysis, experimentation approaches, and data modeling techniques.
• Provide structured, actionable feedback to improve AI model performance.

Skills

Python

Statistics

Machine learning

Data analysis

Pandas

Data modeling

Experimentation

Written communication

Tools

Pandas

Job description

Synthires is seeking a Data Scientist for a remote, contractor engagement to support an AI research project. You will evaluate model performance across statistics, machine learning, experimentation, and quantitative reasoning, contributing to accuracy and analytical capabilities.

Your duties include creating prompts and datasets, identifying methodological flaws, and delivering structured feedback to guide improvements.