Junior Data Scientists, Quantitative Analysts & Machine Learning Researchers. Remote AI Project
We are seeking experienced Data Scientists, Quantitative Analysts, Machine Learning Researchers, and Statistical Modeling Professionals to contribute to a remote AI project focused on statistics, machine learning, experimentation, and quantitative reasoning.
You will evaluate AI-generated solutions to technical and quantitative problems, identify flawed methodology, create challenging prompts and datasets, develop reference solutions, and provide structured feedback that improves model performance.
What You’ll Do
- Evaluate AI-generated responses across data science, statistics, machine learning, experimentation, and quantitative analysis.
- Identify incorrect assumptions, statistical errors, flawed methodology, weak experimental design, and unsupported conclusions.
- Create challenging expert-level prompts based on realistic analytical and quantitative problems.
- Build or review datasets used to evaluate model performance.
- Develop reference solutions that demonstrate correct methodology and reasoning.
- Assess whether analytical approaches are statistically sound, reproducible, and appropriate for the problem.
- Review model reasoning related to experimentation, causal inference, forecasting, predictive modeling, and data interpretation.
- Evaluate code and analytical workflows when relevant.
- Provide structured technical feedback explaining why an approach is correct or incorrect.
- Help refine evaluation criteria and improve the difficulty and quality of quantitative tasks.
Who Can Apply
Relevant backgrounds include Data Scientists, Senior Data Analysts, Junior Data Scientists, Associate Data Scientists, Quantitative Analysts, Quantitative Researchers, and Applied Scientists.
We also welcome Machine Learning Scientists, Machine Learning Engineers, Research Scientists, and Applied Machine Learning Researchers with strong quantitative foundations.
Statistics backgrounds may include Statisticians, Applied Statisticians, Biostatisticians, Statistical Analysts, and Statistical Modeling Specialists.
Experimentation backgrounds may include Experimentation Scientists, Product Data Scientists, Decision Scientists, Causal Inference Specialists, and A/B Testing Experts.
Finance backgrounds may include Quantitative Researchers, Quantitative Strategists, Financial Data Scientists, Risk Modelers, and Quantitative Finance Professionals.
Additional relevant backgrounds include Econometricians, Operations Research Scientists, Analytics Scientists, Research Analysts, and Computational Researchers with strong statistical expertise.
Requirements
- At least one year of professional experience in data science, quantitative analysis, statistics, machine learning, finance, research, or a closely related field.
- At least part of that experience must have occurred within the past three years.
- Strong foundation in statistics, probability, and quantitative reasoning.
- Experience analyzing real-world datasets and interpreting results.
- Ability to identify flawed methodology and statistical errors.
- Strong understanding of experimentation or model evaluation principles.
- Working knowledge of Python, R, SQL, or comparable quantitative tools.
- Strong analytical and problem-solving skills.
- Ability to communicate technical and statistical concepts clearly in writing.
- Comfortable working independently in a remote environment.
Preferred Background
- Professional experience at a leading technology company, financial institution, research organization, or quantitatively rigorous company.
- Experience in product data science, experimentation, quantitative research, applied machine learning, statistics, or analytics.
- Experience designing or reviewing A/B tests, predictive models, or quantitative research.
- Experience with causal inference, forecasting, recommendation systems, risk modeling, or optimization is valuable.
- Experience reviewing technical analyses, research reports, or model outputs for methodological quality.
- Experience creating technical assessments, analytical case studies, interview problems, or quantitative benchmarks is helpful.
This opportunity is ideal for quantitative professionals who can look at a statistical or machine learning problem, identify whether the methodology is genuinely sound, catch subtle errors in assumptions or evaluation, build a rigorous reference solution, and clearly explain why one analytical approach is stronger than another. We are a referral partner of the client