Job description

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 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. ABOUT THE WORK ENVIRONMENT This role is in office at our corporate headquarters in the Brickell area of Miami, Florida. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Requirements

Must-haves

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.
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.
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.

Nice-to-haves

Bachelor’s or master’s degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, Business Analytics, or a related field; advanced degree preferred.
Experience applying large language models, natural language processing, retrieval-augmented generation, intelligent agents, or evaluation methods for AI-enabled products is strongly preferred.
Experience with Airflow or Luigi, Git, CI/CD, cloud infrastructure, Tableau, Metabase, semantic layers, knowledge bases, embeddings, or metadata management is preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaipythonsqlci/cd

Platform: greenhouse
Effort: quick