Principal Data Scientist

Overview

As Principal Data Scientist at Vortexa, you lead high-impact data science work on frontier energy problems, shaping production-grade AI/ML solutions. You collaborate with analysts, engineers, traders, and regulators to ensure reliable, robust models for real-time energy insights. You define success criteria where none exist and drive research into practical, scalable outcomes. You mentor teams, set technical standards, and elevate the organization’s data science capabilities.

Pay / Benefits

• Flexible hybrid work
• Diverse and ambitious culture
• Opportunity to work with top minds
• Equity options for employees
• Private health insurance via Vitality
• Global volunteering policy

Responsibilities

• Tackle frontier problems with no established baselines and define success criteria
• Improve live model performance by collaborating with production pods
• Own end-to-end projects from exploration to long-term maintenance
• Translate research into practical, production-ready solutions that work in real-world settings
• Build and elevate Data Science capability through review, pairing, and mentoring
• Lead the Data Science Guild to set technical direction and resolve cross-cutting questions

Key requirements

• Strategic, high-impact individual contributor with hands-on production-grade model delivery
• Strong ML/AI theory and mathematical foundations with engagement in current research
• Engineering-minded across the full ML lifecycle: design, development, validation, deployment, monitoring
• PhD-level depth in Computer Science, Statistics, Applied Mathematics, Physics, or related field (or equivalent industry depth)
• Proficiency in Python and broad modelling toolkit (regression, classification, clustering, time-series, anomaly detection, seq2seq, stochastic optimization)
• Ability to solve ambiguous problems, define approach, and take ownership of next steps
• Ability to mentor, review, pair-program, and raise team capability
• Credible with non-technical stakeholders, able to negotiate scope and explain trade-offs

• Strategic thinking
• Mentoring and teaching
• Effective communication with technical and non-technical stakeholders
• Python
• Regression
• Classification

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