Senior Data Scientist — Remote AI Evaluation & Modeling

Synthires; Greater London; Remote; GBP 153,000 - 205,000; Full time

Benefits offered by this job

Fully remote

100 openings

Job summary

Synthires is seeking Data Scientists and Quantitative Professionals to support an AI research project. You’ll evaluate AI model performance across statistics, machine learning, experimentation, and quantitative reasoning to help improve model accuracy and analytical capabilities.

Key tasks include creating challenging prompts, datasets, and reference solutions; identifying flawed methodologies and statistical errors; and assessing data analysis, experimentation approaches, and data modeling

Qualifications

• Minimum 1 year of experience at a leading company in technology, finance, research, or related field 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; ability to communicate statistical concepts 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.

Skills

Python

Statistics

Machine Learning

Data Analysis

Experimentation

Quantitative Reasoning

Communication

Tools

Pandas

Job description

Synthires is seeking Data Scientists and Quantitative Professionals to support an AI research project. You’ll evaluate AI model performance across statistics, machine learning, experimentation, and quantitative reasoning to help improve model accuracy and analytical capabilities.

Key tasks include creating challenging prompts, datasets, and reference solutions; identifying flawed methodologies and statistical errors; and assessing data analysis, experimentation approaches, and data modeling