Senior/Staff Data Scientist (Optimisation)
The Role
We're hiring a Senior or Staff-level Data Scientist to help design and build the powerflow and 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 forecasts and 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 rest of the Grid Modelling team to translate network physics into optimisation constraints and get solutions into production.
What you'll do
Develop powerflow engines for grid modelling and forecasting
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
What you'll bring
Powerflow and/or optimisation expertise: Deep experience with one or both of
AC optimal powerflow (AC-OPF) calculations, including the use of key open-source packages such as OpenDSS, pandapower and lf-energy,
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 data science/ML/optimisation roles with demonstrated impact
Python proficiency: Capable of writing quality code for optimisation algorithms and data analysis; familiar with architectural principles, system design, version control, testing practices, code review, CI/CD
What would set you apart
Grid domain knowledge (electrical networks, power systems, operational constraints)
Experience with market dispatch, energy economics, or resource scheduling problems
Real-time or near-real-time optimisation system experience
Understanding of DER integration challenges and VPP operations
Experience in highly parametric statistical model fitting, e.g. state estimation for energy networks
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
Familiarity with data cleaning and data exploration
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.
We're hiring a Senior or Staff-level Data Scientist to help design and build the powerflow and 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 forecasts and 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 rest of the Grid Modelling team to translate network physics into optimisation constraints and get solutions into production.
What you'll do
Develop powerflow engines for grid modelling and forecasting
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
What you'll bring
Powerflow and/or optimisation expertise: Deep experience with one or both of
AC optimal powerflow (AC-OPF) calculations, including the use of key open-source packages such as OpenDSS, pandapower and lf-energy,
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 data science/ML/optimisation roles with demonstrated impact
Python proficiency: Capable of writing quality code for optimisation algorithms and data analysis; familiar with architectural principles, system design, version control, testing practices, code review, CI/CD
What would set you apart
Grid domain knowledge (electrical networks, power systems, operational constraints)
Experience with market dispatch, energy economics, or resource scheduling problems
Real-time or near-real-time optimisation system experience
Understanding of DER integration challenges and VPP operations
Experience in highly parametric statistical model fitting, e.g. state estimation for energy networks
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
Familiarity with data cleaning and data exploration
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.