Remote | Data Scientist & Quantitative Analyst — $55–$85/hour

24-Mag•New York, NY

4d•Remote

About The Position

We are sharing a specialised full-time consulting opportunity for experienced data scientists and quantitative analysts with strong expertise in statistical analysis, data cleaning, method comparison, reproducible research, and evidence-based reporting. This role supports the development of advanced agentic evaluation benchmarks for frontier AI models. Selected professionals will create realistic data-analysis challenges, develop reproducible reference notebooks, evaluate model-generated analyses, and identify where statistical reasoning, interpretation, or reporting falls short of professional standards.

Requirements

At least 1 year of experience in data science, quantitative analysis, research engineering, or another research-intensive analytical role
Deep hands-on experience with data cleaning, statistical correlation, hypothesis testing, and interpretation
Strong proficiency in Python, including pandas, NumPy, or comparable analytical libraries
Experience using Jupyter Notebook or Google Colab for analysis and reporting
Working familiarity with Git and reproducible analytical workflows
Ability to communicate complex quantitative findings clearly to technical and non-technical decision-makers
Strong attention to detail and confidence working through ambiguous, open-ended problems
Reliable availability for approximately 35 hours per week

Nice To Haves

Experience in AI training, model evaluation, or benchmark development
Background authoring analytical tasks, reference solutions, or grading rubrics
Familiarity with anomaly detection, experimental design, or comparative model evaluation
Experience conducting manual spot checks and validating automated analyses
Knowledge of statistical modelling, machine learning, or scientific computing
Familiarity with agentic AI systems and multi-step model evaluations
Experience reviewing notebooks, code, or analyses prepared by other professionals
Strong ability to identify subtle statistical errors and unsupported conclusions

Responsibilities

Create realistic analytical tasks based on professional data science and quantitative research workflows
Develop assignments involving messy data, anomaly detection, correlation analysis, hypothesis testing, and method comparison
Design complex, multi-step problems requiring statistical judgment and careful interpretation
Ensure tasks include realistic constraints, datasets, assumptions, and decision-making objectives
Complete reference analyses using Jupyter Notebook or Google Colab
Build clear and reproducible workflows using Python, pandas, NumPy, and related libraries
Document data-cleaning decisions, calculations, statistical methods, and analytical conclusions
Validate intermediate results, spot checks, visualisations, and final recommendations
Design fair comparisons between analytical models, algorithms, or statistical approaches
Evaluate performance using appropriate metrics, manual checks, and sensitivity analyses
Identify methodological trade-offs, limitations, and sources of uncertainty
Produce recommendations supported by transparent quantitative evidence
Review model-generated analyses for statistical accuracy, methodological rigour, and sound interpretation
Verify whether calculations, correlations, hypotheses, and conclusions are supported by the data
Identify coding errors, unsupported assumptions, misleading summaries, and analytical shortcuts
Explain where and why model outputs fail to meet professional data-analysis standards
Work closely with researchers, task authors, and fellow quantitative specialists
Compare evaluation decisions to maintain consistent benchmark standards
Refine tasks, reference notebooks, and grading criteria based on testing outcomes
Document recurring model weaknesses and opportunities for stronger evaluation coverage