Senior/Staff Data Scientist (Optimisation)

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's vertical SaaS platform uses data, ML and AI to help grid operators modernise their operations and unlock transformational new capabilities. We deliver visibility into the grid edge, faster renewable connections, and real-time orchestration of distributed energy. The opportunity is enormous ($1B+ ARR).

We're embedded with over 50% of Australia's distribution networks, more than 30% in New Zealand, and have 1 of the top 5 utilities in the US as our lighthouse. Airtree led our Series A, we grew ARR 3.4x in the past 6 months, and we're on the path to build the first truly generational utility software company.

We are a team of builders. What unites us is that we’re curious, hungry and obsessed with maximising the impact we can deliver to Gridsight and the energy transition. If that sounds like you, come join us!

The Role

As a Senior/Staff Data Scientist (Optimisation), you will design, implement and run the optimisation engines that power modern dynamic electricity grids. Your solutions will allow electricity utilities to efficiently manage grid constraints and resources, to forecast grid state into the future, and to assess connection applications for new renewables projects in record time.

Key Accountabilities

Solve constrained optimisation problems related to grid operations, resource dispatch, and network management

Design optimisation solutions that balance multiple objectives (safety, efficiency, customer impact, operational constraints)

Collaborate with others in the grid modeling team to formulate network models and constraints as optimisation problems

Build scalable optimisation solutions that perform in real-time or near-real-time operational contexts

Establish best practices for optimisation model development, validation, and monitoring

Contribute to technical strategy for data science, optimisation capabilities and future applications

Core Requirements

Optimisation fundamentals: Strong understanding of existence and uniqueness of optima, ill-posedness, local vs global optimisation, gradient descent methods, least squares minimisation, non-convex optimisation

Linear optimisation expertise : Deep experience in constrained optimisation, operations research, or similar fields (linear programming, convex optimisation, mixed-integer programming, familiarity with optimisation solvers and frameworks such as Pyomo, CBC, Gurobi, CPLEX, OR-Tools, etc.)

Data science foundations : Statistical modeling, numerical programming, algorithm design, data analysis

Senior/Staff level experience : 5+ years in linear optimisation roles with demonstrated impact

Software engineering prowess : Capable of writing quality code in Python for optimisation algorithms and data analysis ; familiar with architectural principles, system design, version control, testing practices, code review, CI/CD

Differentiators

Grid domain knowledge (electrical networks, power systems, operational constraints)

Experience with network constraints, capacity expansion or resource scheduling problems

Real-time or near-real-time optimisation system experience

Understanding of DER integration challenges and battery/VPP operations