Data Scientist

ABOUT THE ROLE

As Principal, Data Scientist, you will lead the development of advanced analytical and machine learning solutions that help Hut 8 make better decisions, improve performance, and create new products and capabilities. You will work across the organization to frame ambiguous business problems, identify high-value opportunities for data science, and turn complex data into models, insights, and decision-support products with measurable impact.

This is a hands-on technical leadership role. You will shape the data science roadmap, establish strong modeling and experimentation practices, and serve as a senior advisor to business and technical leaders. You will partner with Data Operations and Software Engineering to ensure reliable access to well-defined data, while maintaining primary ownership of the analytical approach, model quality, and business outcomes.

Some of the key responsibilities you should expect are the following:

• Identify and prioritize high-impact opportunities for data science across Hut 8’s technology, energy, infrastructure, AI, colocation, cloud, and mining businesses.
• Translate business questions into clear analytical frameworks, hypotheses, modeling strategies, success metrics, and measurable acceptance criteria.
• Develop, validate, and deploy predictive, forecasting, optimization, classification, ranking, and anomaly-detection models that solve complex business problems.
• Apply statistical analysis, experimental design, hypothesis testing, causal inference, and scenario modeling to evaluate decisions and quantify business impact.
• Lead the end-to-end data science lifecycle, including exploratory analysis, feature engineering, model development, validation, deployment, monitoring, and iteration.
• Build reusable analytical products, decision-support tools, models, reports, and visualizations that enable leaders and operating teams to act with greater speed and confidence.
• Establish standards for model evaluation, interpretability, reproducibility, documentation, experimentation, and responsible use of machine learning.
• Develop evaluation frameworks for AI and agentic workflows, including benchmark datasets, retrieval and response quality measures, groundedness, relevance, and user feedback analysis.
• Partner with Data Operations and Software Engineering on source data requirements, data quality issues, feature pipelines, production integrations, model serving, and monitoring.
• Use data quality findings, model performance, and stakeholder feedback to improve analytical products and ensure they remain reliable and useful over time.
• Communicate technical findings, uncertainty, recommendations, and business impact clearly through concise narratives, visualizations, reports, and executive presentations.