Data Scientist — Mathematical Statistics (Python)

About OpenTrain AI

OpenTrain AI is the #1 platform for building careers in AI training and data labeling. We connect contributors with hands-on work that shapes how modern AI behaves and grows skills used across the industry.

For this role, OpenTrain AI is the hiring organization: you'll join a remote, flexible contracting project that focuses on rigorous statistical analysis to improve model development and evaluation.

About AI Training Work

AI training (data labeling / annotation / human feedback) is the human core of machine learning — people prepare, evaluate, and refine examples that models learn from. Tasks range from annotating images and audio to writing, rating, and statistically evaluating data and model outputs.

This project sits at the intersection of data science and evaluation: you will apply mathematical-statistics methods to produce reliable analyses that inform model decisions and experiments.

The Role

We’re hiring an entry-level Data Scientist with a strong foundation in mathematical statistics and advanced Python analysis libraries. This is a part-time contractor role for under 20 hours per week with pay at $25/hour.

You will clean and wrangle datasets, select and run appropriate statistical tests using scipy and statsmodels, fit regression models, assess diagnostics, compute effect sizes and power, and deliver clear, reproducible summaries of results.

What You’ll Do

• Clean and preprocess messy datasets using pandas and numpy to prepare data for analysis.
• Select and run hypothesis tests (t-test, chi-square, ANOVA) and appropriate post-hoc analyses when needed.
• Fit linear and logistic regression models with statsmodels, check assumptions, and run diagnostic analyses.
• Compute and interpret p-values, confidence intervals, effect sizes, and power analyses for experiments and A/B tests.
• Use correlation methods (Pearson and Spearman) and explore probability distributions and non-parametric alternatives.
• Summarize findings with clear narratives and visual summaries; document assumptions and limitations.
• Maintain reproducible notebooks and document methods, assumptions, code, and results for stakeholders.
• Perform evaluation and rating tasks that require statistical interpretation and judgment where applicable.

Requirements

• Strong Python for analysis: numpy, scipy, statsmodels, and pandas required.
• Mastery of hypothesis testing (t-test, chi-square, ANOVA) and post-hoc approaches as appropriate.
• Ability to calculate and interpret p-values, confidence intervals, and effect sizes.
• Proficient with correlation analysis (Pearson/Spearman) and regression modeling (linear and logistic).
• Comfortable with probability distributions, normality checks, and non-parametric methods.
• Experience with exploratory data analysis (EDA), cleaning, and extracting insights from messy data.
• Solid understanding of experimental design, A/B testing, and power analysis.
• Reproducible workflows: experience with notebooks, version control, and clear documentation.
• Strong analytical writing and ability to explain assumptions and limitations clearly.
• SQL for data extraction and joins is a plus but not required.

Who Should Apply

This role is entry level and well suited to early-career data scientists, statisticians, or analysts who are proficient in Python and eager to apply mathematical-statistics skills in real projects.

Worldwide applicants are welcome. The role is remote, flexible, and part-time (less than 20 hours per week) as a contractor.

Compensation, Schedule, and Next Steps

Pay is $25 per hour. The role is contract, part-time, and expected to average under 20 hours per week.

To apply, create an OpenTrain account, complete your profile, and submit your application and examples of statistical work or reproducible notebooks if available. OpenTrain connects you directly to the project and provides the platform to manage work and deliverables.