Senior Data Scientist | Perm | Melbourne | Utilities

Background

Our client is a growing data and AI business focused on delivering long-term value through machine learning, advanced analytics and modern data platforms. By combining diverse datasets, domain expertise and cloud technology, they help organisations solve complex operational and strategic challenges.

Since early 2024, they have been expanding in Melbourne, drawn by the city’s central role in Australia’s energy transition and the chance to support critical infrastructure. In that time they have worked with some of the most innovative utility providers in the Asia-Pacific region on projects that put data engineering and machine learning at the heart of infrastructure modernisation. With demand growing, they are now building out their local team.

Why Join?

Our client combines the technical depth of a high-performing data science and engineering team with access to large-scale, real-world operational problems across utilities, infrastructure, telecommunications and retail. They work with leading platforms including Databricks and have delivered both machine learning and generative AI solutions into production, beyond proof of concept. Every role contributes directly to solutions with real operational outcomes, and you will work alongside curious, capable peers on technically rewarding problems.

How are they making an impact?

Our client is helping a major electricity distributor change how it assesses new connections and plans its network. The work covers the full process: building a smart meter data platform, developing supervised and unsupervised machine learning models, and integrating their predictions into business decisions.

Unsupervised learning identifies patterns in electricity use, while supervised learning predicts the consumption and demand profiles of new customers before they connect. These models directly inform connection assessments and network planning, influencing connection revenues, assessment costs and investment in network upgrades. The result is faster connections, less manual work and better use of existing infrastructure, ultimately leading to a better and cheaper service for customers.

They are also integrating generative AI tools so business users can ask questions in plain language and reach data and model outputs through their everyday workflows.

Role Overview

- Develop and deploy supervised and unsupervised machine learning models that support operational and investment decisions for utility providers
- Work across the full project lifecycle, from understanding business requirements and exploring data to engineering features, evaluating models and integrating predictions into business processes
- Join a team of data scientists and engineers working with smart meter measurements, customer applications,



geospatial data and network asset records
- Use Python, PySpark, Databricks and MLflow to build reliable solutions, with generative AI tools helping business users access data and model outputs

Key Responsibilities

- Work directly with client stakeholders to define the problem, understand the decisions being supported and agree how success will be measured
- Explore and assess complex datasets, investigate data quality issues and engineer features that capture relevant customer and operational characteristics
- Develop supervised and unsupervised models, establish meaningful baselines and evaluate performance using methods that reflect how the models will be used in practice
- Take ownership of modelling work from initial investigation through to production use, working with data engineers to build reliable training and prediction pipelines
- Write maintainable, tested code and manage deployments using Git, CI/CD and Declarative Automation Bundles
- Monitor model performance, investigate errors and improve models as new data and business requirements emerge
- Explain results, uncertainty and limitations clearly so stakeholders can make informed decisions
- Apply generative AI where it improves access to data, models and analysis within business workflows
- Help client colleagues build practical data science and machine learning skills through joint development, code reviews and focused training
- Review colleagues’ work and help establish consistent modelling and engineering practices

Qualifications

- Degree in a quantitative discipline such as engineering, computer science, mathematics, statistics or physics, or equivalent professional experience demonstrating strong mathematical, statistical and machine learning foundations
- Existing Australian work rights are strongly preferred, given the required start date

Essential Experience

- Strong Python skills, including practical experience with pandas, scikit-learn, NumPy and SciPy
- Substantial experience applying supervised and unsupervised machine learning, with sound judgement about which methods suit a problem
- Strong understanding of the mathematical and statistical foundations of machine learning, including model assumptions, uncertainty and limitations
- Experience exploring complex datasets, investigating data quality issues and engineering features based on an understanding of the underlying problem
- Experience building complete machine learning pipelines, including preprocessing,



training, hyperparameter optimisation and evaluation
- Ability to design validation approaches that reflect real use, prevent data leakage and assess performance against meaningful baselines and business requirements
- Experience deploying models into production and supporting their ongoing use, including investigating performance issues and identifying when updates are needed
- Experience with MLflow or similar tools for experiment tracking, reproducibility and model registration
- Experience with Databricks and cloud services such as AWS or Azure
- Practical experience using PySpark for distributed processing of large datasets
- Experience writing maintainable code using Git, code review, automated testing and CI/CD
- Ability to work directly with stakeholders and collaborate with engineers to turn business requirements into working solutions

Desirable

- Experience in electricity distribution, electrical engineering or other utility sectors
- Experience analysing smart meter, time series, geospatial or network asset data
- Experience implementing model monitoring, drift detection and retraining workflows
- Familiarity with Databricks Unity Catalog and Declarative Automation Bundles
- Practical experience using generative AI, including Databricks Genie, LLMs, agents or retrieval-augmented generation, to solve business problems
- Experience coaching or training others in applied data science, machine learning or software development

Skills and Approach

- Explains technical findings, trade-offs and limitations clearly to technical and non-technical audiences
- Plans and delivers work independently, keeps stakeholders informed and raises risks or blockers early
- Questions assumptions, investigates problems thoroughly and chooses practical solutions that meet business needs
- Takes responsibility for seeing work through to completion and checking that it delivers the intended results
- Documents methods and decisions clearly, shares knowledge and gives and receives constructive feedback

Benefits

Our client offers the backing of an established business combined with the responsibility and direct involvement of a small technical team.

- Competitive salary and performance-based bonus scheme
- Annual leave entitlement, plus applicable public holidays
- Employer-paid superannuation contributions
- Learning and development budget for certifications, courses and new technical skills
- Hybrid working, balancing time with colleagues and clients with work from home
- Access to Databricks and cloud infrastructure to develop and deploy solutions
- Laptop and equipment provided
- Opportunities to deepen your expertise in applied machine learning, generative AI and utilities
- A team-oriented team with regular knowledge sharing and team events

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