Remote | Data Scientist (Analytics & Machine Learning) — $50–$60​/hour

Specialised Part-Time Consulting Opportunity

We are sharing a specialised part-time consulting opportunity for experienced data science and analytics professionals with strong expertise in machine learning, statistical modelling, experimentation, data infrastructure, business intelligence, and enterprise analytics.

This role supports advanced AI evaluation work focused on realistic large-scale data science and analytics workflows. Selected professionals will design complex technical scenarios, develop reference outputs, and create evaluation rubrics that reflect how experienced data scientists and analytics leaders approach modelling, experimentation, data strategy, and production-scale data systems.

Machine Learning & Predictive Modelling

• Develop realistic scenarios involving predictive modelling and machine learning development
• Create tasks covering feature engineering, model selection, training, validation, and performance evaluation
• Assess modelling approaches for statistical validity, scalability, and business relevance
• Identify weak assumptions, methodological issues, and potential sources of bias or leakage
• Produce reference analyses and model documentation demonstrating strong technical judgement

Statistical Analysis & Experimentation

• Develop tasks involving statistical inference, A/B testing, experimentation, and causal analysis
• Evaluate experimental designs, metrics, sampling approaches, and statistical conclusions
• Create scenarios involving ambiguous or noisy real-world datasets
• Assess whether analytical conclusions are adequately supported by available evidence
• Produce reference analyses explaining methodological choices and limitations

Data Engineering & Analytics Infrastructure

• Create scenarios involving enterprise data pipelines, data transformation, and analytics infrastructure
• Apply familiarity with platforms such as Snowflake, Databricks, and comparable data environments
• Develop tasks involving SQL, Python, R, and large-scale data-processing workflows
• Evaluate data architecture and pipeline decisions for reliability, scalability, and analytical usability
• Identify data-quality, lineage, governance, and integration issues

Business Intelligence & Decision Support

• Develop scenarios involving enterprise reporting, dashboards, and business intelligence
• Create tasks using tools such as Tableau, Power BI, or comparable analytics platforms
• Evaluate metrics, visualisations, and analytical outputs for clarity and decision usefulness
• Translate complex analytical findings into actionable business insights
• Produce reference executive-level analyses and recommendations

MLOps & Production Analytics

• Develop scenarios involving deployment, monitoring, validation, and lifecycle management of machine learning models
• Apply familiarity with platforms such as Sage Maker, Vertex AI, MLflow, or comparable tools
• Evaluate model-monitoring strategies, retraining approaches, and production performance
• Assess operational risks including model drift, data changes, and reliability issues
• Apply MLOps best practices to realistic enterprise machine learning environments

Data Strategy & Governance

• Create tasks involving enterprise data strategy, analytics governance, and cross-functional decision-making
• Evaluate approaches to data quality, privacy, access, governance, and analytical consistency
• Develop scenarios involving competing stakeholder and business requirements
• Assess strategic trade-offs across technical capability, risk, cost, and organisational priorities
• Produce reference recommendations reflecting senior-level data and analytics judgement

AI Evaluation & Rubric Development

• Design expert-level evaluation tasks across data science and analytics workflows
• Draft reference outputs demonstrating authentic professional technical judgement
• Develop rubrics that distinguish experienced data science reasoning from generic textbook or tutorial-level responses
• Review AI-generated outputs for statistical accuracy, technical quality, and practical relevance
• Identify subtle analytical errors, unsupported conclusions, and unrealistic technical recommendations
• Provide clear written rationale supporting evaluation decisions

Ideal…