Principal Data Scientist

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.

• 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

• 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

• 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