Remote Survey Statistician for AI Evaluation | Flexible Hours

Talantir; Quebec; Remote; CAD 77,000 - 116,000; Full time

Benefits offered by this job

Fully remote

Flexible schedule

Competitive pay: hourly $40–$60

Job summary

Talantir is seeking an experienced Survey Statistician - AI Trainer to help advance AI development while working fully remote with a flexible schedule. You will apply rigorous quantitative methods to evaluate AI-generated analyses and contribute to model benchmarking.

You’ll design quantitative tasks, write clear analytical code, and provide practical feedback that shapes how AI systems reason about data, forecasting, and experiments.

Qualifications

• 2+ years in a quantitative role or research environment (data science, statistics, economics, etc.).
• Some coding experience with end-to-end analytical code.
• Experience with statistical methods, predictive modeling, and experiment design (A/B testing, hypothesis testing, regression, time-series).
• Fluency in English with strong writing skills.
• Bachelor's degree in a quantitative field; Master/PhD a plus.
• Credentials like Kaggle rankings or cloud ML certs are a plus.

Responsibilities

• Evaluate AI-generated quantitative work for technical accuracy and real-world validity.
• Design and solve quantitative problems to train and benchmark AI systems across forecasting, experiments, optimization, and inference.
• Write clear technical explanations and well-documented analytical code.
• Provide feedback to shape the next generation of AI models for quantitative reasoning.

Skills

Quantitative analysis

Statistical methods

A/B testing

Time-series forecasting

Predictive modeling

Data science

English writing

Education

Bachelor's degree in a quantitative field

Tools

Python

R

Job description

Talantir is seeking an experienced Survey Statistician - AI Trainer to help advance AI development while working fully remote with a flexible schedule. You will apply rigorous quantitative methods to evaluate AI-generated analyses and contribute to model benchmarking.

You’ll design quantitative tasks, write clear analytical code, and provide practical feedback that shapes how AI systems reason about data, forecasting, and experiments.