Job Description:
• 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
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
• A master's degree or PhD in statistics, data science, mathematics, economics, computer science, engineering, or another quantitative discipline is highly relevant.
• Equivalent practical experience in a research-heavy analytical field may also be considered.
• Academic or professional research involving statistical modelling, experimentation, or large-scale data analysis may strengthen an application.
• Publications, technical reports, open-source work, or impactful analytical projects may also be valuable.
Benefits:
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