Listing evidence and provenance
- Source
- greenhouse View original listing
- Freshness
- Open in the current public read path; this is not an employer guarantee.Last observed September 29, 2026
- Classification
- Modern power-system classification not yet published for this listing.Legacy source industry field: Energy Management
- Pay provenance
- Estimated benchmark; not employer-provided
Job Description
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.
Key Responsibilities
- 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.
Requirements
- Bachelor’s or master’s degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, Business Analytics, or a related field; advanced degree preferred.
- 6+ years of progressive experience in data science, machine learning, advanced analytics, or a closely related discipline, with a demonstrated record of leading high-impact initiatives.
- Strong proficiency in Python and SQL, with advanced experience using statistical and machine learning libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, or comparable tools.
- Deep understanding of statistical modeling, hypothesis testing, experimental design, model evaluation, feature engineering, and machine learning fundamentals.
- Experience developing and operationalizing models in production, including model versioning, reproducibility, monitoring, performance evaluation, and collaboration with engineering teams.
- Demonstrated ability to work with time-series data, forecasting, optimization, anomaly detection, or other analytical methods relevant to complex operational and business problems.
- Experience applying large language models, natural language processing, retrieval-augmented generation, intelligent agents, or evaluation methods for AI-enabled products is strongly preferred.
- Strong ability to move from ambiguous business problems to rigorous analytical approaches, practical recommendations, and measurable outcomes.
- Familiarity with cloud data platforms such as Snowflake or BigQuery, data warehouses, data modeling, data quality, and lineage concepts.
- Excellent written and verbal communication skills, including the ability to explain technical concepts, model limitations, uncertainty, and tradeoffs to non-technical stakeholders and senior leaders.
- Demonstrated technical leadership, mentoring ability, and success working across business, data, software, and infrastructure teams.
- Experience with Airflow or Luigi, Git, CI/CD, cloud infrastructure, Tableau, Metabase, semantic layers, knowledge bases, embeddings, or metadata management is preferred.
Ready to Apply?
Apply directly on Hut 8's site
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