About Gridsight

Electricity grids are undergoing the most significant transformation in a century. The shift to renewables, the proliferation of rooftop solar, batteries, and EVs, and the increasing complexity of distribution networks are forcing utilities to operate their grids in fundamentally new ways.

Gridsight builds the data engine that makes this possible. We're already embedded with most of Australia's major distribution networks and have contracts with some of the largest investor-owned utilities in the United States. We're well-funded, growing quickly, and positioned at the centre of a global shift toward dynamic grid management.

The Role

We're hiring a Senior or Staff-level Data Scientist to design and build the optimisation engines at the core of our platform.

This role sits at the heart of our work on calculating safe, real-time capacity for distributed energy resources within network constraints and turning those calculations into dispatch, resource allocation, and grid management decisions that affect how networks actually operate.

This is applied optimisation work. You'll formulate constrained optimisation problems, implement algorithms, and build systems that run in real-time or near-real-time operational contexts. You'll work closely with the grid modelling team to translate network physics into optimisation constraints, and with software engineers to get solutions into production.

What You'll Do

• Develop optimisation algorithms and engines for dynamic operating envelopes that manage DER capacity within network constraints
• Formulate and solve constrained optimisation problems across grid operations, resource dispatch, and network management
• Design solutions that balance competing objectives — safety, efficiency, customer impact, and operational constraints
• Collaborate with the grid modelling team to translate network models into tractable optimisation formulations
• Build optimisation systems that perform reliably at operational timescales
• Contribute to technical strategy and help establish best practices for optimisation development, validation, and monitoring

What You'll Bring

• Strong data science foundations: statistical modelling, experimental design, model validation, feature engineering, ML techniques
• Deep optimisation expertise: constrained optimisation, operations research, linear programming, convex optimisation, mixed-integer programming, model predictive control, or metaheuristics
• 5+ years in data science, ML, or optimisation roles, with demonstrated impact at a senior or staff level
• Python proficiency and the ability to write quality code for optimisation algorithms and analysis

What Would Set You Apart

• Understanding of electrical distribution networks, power systems, and grid physics
• Experience with market dispatch, energy economics, or resource scheduling problems
• Software engineering depth — system design, testing, deployment, MLOps
• Familiarity with optimisation solvers and frameworks (Pyomo, Gurobi, CPLEX, CBC, OR-Tools, etc.)
• Real-time or near-real-time optimisation system experience
• Understanding of DER integration challenges and VPP operations

What We Offer

• $160k–$220k depending on experience, plus equity
• Remote-first, with head office in Sydney
• A talented team of engineers, data scientists, and power systems specialists working on hard problems that matter

Sound Interesting?

We'd love to hear from you! Apply directly and we'll be in touch shortly.