Asks for

The ad says“Data science foundations: Statistical modeling, numerical programming, algorithm design, data analysis”
The ad says“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.)”
The ad says“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”
The ad says“Solve constrained optimisation problems related to grid operations, resource dispatch, and network management”
The ad says“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.)”
The ad says“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.)”
The ad says“Data science foundations: Statistical modeling, numerical programming, algorithm design, data analysis”
The ad says“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”
The ad says“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.)”
The ad says“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”
The ad says“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.)”
The ad says“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.)”
The ad says“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”
The ad says“Data science foundations: Statistical modeling, numerical programming, algorithm design, data analysis”
The ad says“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.)”
The ad says“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.)”
The ad says“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”
The ad says“Data science foundations: Statistical modeling, numerical programming, algorithm design, data analysis”

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
Experience with optimisation in high-performance contexts, e.g. use of metaheuristics, HPC
Experience in load forecasting
Ability to work with customers to incorporate their existing forecasts into your own calculations
Software engineering depth (system design, testing, deployment, MLOps)
Familiarity with Databricks
Experience instrumenting and analysing code performance with observability tools (e.g. Datadog, Grafana)
Familiarity with data cleaning and data exploration

What We Offer

Competitive salary. We hold high expectations, but pay accordingly.
Meaningful ESOP so you share in the growth of Gridsight.
$2,500 WFH allowance, $2,500 health and wellness allowance, bi-annual in person working weeks, and lunch is on us when you’re in office.
Work how you like. Fully remote, hybrid or in office with a fresh, new head office in Sydney and satellite spaces in Melbourne, Brisbane and Hobart.

Senior/Staff Data Scientist (Optimisation)