Data Scientist, Principal

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. Mentor data scientists and raise the organization’s capabilities in statistical thinking, machine learning, experimentation, and data-driven decision-making. ABOUT YOU Bachelor’s or master’s…