Senior/Staff Data Scientist

Gridsight is a rapidly growing Grid/CleanTech startup on a mission to accelerate global electrification and decarbonisation. We are building a vertical SaaS platform for electricity utilities, enabling them to modernise grid operations and unlock transformational flexibility capabilities such as dynamic operating envelopes and flexible interconnections. Having recently raised our Series A funding from Airtree Ventures, Energy Transition Ventures and Area VC, we are poised for rapid growth and are seeking talented individuals to join us on our mission.

Purpose Design and develop software and data science tools for modelling usage and generation of electricity, enabling electrical utilities to manage the growth and operation of their distribution networks through advanced data analytics.

Key Accountabilities

Design, build, and maintain software products and data science models capable of simulating or predicting key components, properties and events of electricity grids, including individual connections to the grid such as power assets, commercial, industrial, agricultural and infrastructure projects

Obtain data from disparate sources to inform and support modelling, by performing data discovery and/or developing automated retrieval pipelines as needed

Ensure software and model capabilities, coverage and performance align with customer requirements, directly engaging with customers from time to time in order to do so

Collaborate with Software Engineers, Data Engineers, and other Product teams to understand data science requirements and translate them into technical solutions

Establish and follow data, scientific, MLOps and software engineering best practices including testing, validation, documentation, monitoring, version control and code reviews

Contribute to data science strategy and architectural decisions

Core Requirements

Senior/staff level experience: 5+ years in data science roles with demonstrable impact

Software engineering fundamentals: proficiency in Python or similar language, architecture, system design, version control testing practices, code review, CI/CD

Numerical modelling: mathematical modelling, numerical methods, applied statistics

ML modelling: experience developing, training, testing and validating machine learning models, as well as deploying them to production

Customer-facing experience: experience delivering software/data products to customers, and iterating based on their feedback.

Differentiators

Experience building data products for customer consumption

Experience in energy, utilities, or IoT/sensor data domains

Experience with time-series data or operational analytics

Experience with business process modelling

Experience discovering and integrating diverse data sources into complex modelling pipelines, both ML and otherwise

Experience designing and implementing agentic workflows

Experience optimising Python workflows, e.g. via parallel/distributed solutions or development of interfaces to optimised lower-level libraries

Experience working in remote or distributed teams

Data governance or compliance experience in regulated industries

Knowledge, Skills & Attributes Knowledge N/A

Data Science

Data exploration and cleaning

Numerical analysis, numerical modelling, numerical programming

Statistical modelling and metrics

Machine learning model architectures and techniques for training, testing, validation and optimisation

Scientific design of analysis and simulation strategies

Software Engineering

Version control workflows with git and branching strategies

Testing practices — unit, integration, and data quality testing

Code review and collaborative development

CI/CD principles and deployment automation

Refactoring and managing technical debt

Software architecture and system design

Infrastructure & Tools

Python, or strong evidence of an ability to pick it up quickly

Version control: git

Machine learning frameworks: scikit-learn, pytorch, keras, tensorflow or similar

Continuous integration / continuous delivery: GitHub Actions, Gitlab CI/CD, Jenkins, Circle CI or similar

Skills

Data science model design, development and maintenance

Ability to easily translate business logic and abstract concepts into concrete, quantitative modelling and constraint strategies

Software performance optimization, troubleshooting and debugging, particularly in the context of numerical software

Ability to demonstrate good code hygiene in the context of numerical programming: precision, error accumulation, convergence, stability, testing, code clarity, maintainability and documentation.

Technical communication: explaining analyses, modelling and results to varied audiences

Collaboration with cross-functional stakeholders including data engineers, software engineers, product specialists, designers and domain experts

Attributes

Ownership mindset — accountable for performance of grid connection modelling capabilities

Pragmatic — balances technical rigour with delivery realities

Organised — keeps clear and detailed records of decisions made and work done; manages time effectively

Detail-oriented, proactive — catches edge cases, modelling and software issues before they impact users

Solution focused — thrives on ambiguity as an opportunity to deliver clarity and solutions

Customer focused — thinks about data consumers and designs systems that serve their needs effectively

Collaborative — works effectively across disciplines and teams

Growth-oriented — continuously learning and helping others develop

Calm under pressure — navigates competing priorities and interpersonal challenges without losing focus

Locations Sydney, Melbourne, Canberra, Wollongong - hybrid and remote available

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