We are seeking an Early Career Data Scientist to join our engineering team and help solve complex, real-world problems using data, mathematics, and machine learning.
At Syncrowin, you will work with large-scale datasets to develop uncover operational patterns, and support intelligent decision-making across energy, manufacturing, mining, and critical infrastructure.
You will work closely with experienced AI engineers, data scientists, and domain experts, gaining hands‐on exposure to the entire machine learning lifecycle, from data exploration and experimentation to model validation and deployment.
This role is ideal for a recent graduate or early‐career qualified with strong analytical foundations, intellectual curiosity, and a genuine interest in applying data science to challenging real‐world problems.
We value demonstrated ability, initiative, and a willingness to learn over years of experience.
What you will do
Explore, clean, analyse, and interpret large‐scale industrial and operational datasets
Develop and evaluate statistical and machine learning models for forecasting, anomaly detection, and optimisation
Perform exploratory data analysis and identify meaningful patterns in complex datasets
Build data preprocessing, feature engineering, and model evaluation pipelines
Work with time-series, sensor, and operational data from real industrial environments
Design experiments, test hypotheses, and communicate findings clearly
Collaborate with AI engineers to improve model accuracy, robustness, and generalisation
Contribute to research, prototyping, and the development of new AI capabilities
Document methodologies, experiments, and model performance
What you will need
Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, Physics, or a related quantitative discipline
0–3 years of relevant experience, including internships, academic research,



or substantial personal projects
Sound understanding of statistics, probability, linear algebra, and machine learning fundamentals
Experience using SQL to query and manipulate data
Ability to structure analytical problems and draw meaningful conclusions from data
Strong attention to detail and a methodical approach to experimentation
Curiosity, initiative, and willingness to learn new technical concepts quickly
Ability to communicate technical findings clearly
Good to have
Academic or project experience with forecasting, deep learning, or optimisation
Familiarity with PyTorch or TensorFlow
Exposure to cloud platforms, particularly Azure
Experience with sensor data, IoT, energy systems, or industrial datasets
Participation in research projects, Kaggle competitions, or open‐source contributions
Understanding of MLOps, model deployment, or data engineering fundamentals
Experience with mathematical modelling, operations research, or applied optimisation
Work on real industrial datasets and problems with measurable operational impact
Gain hands‐on experience building AI solutions beyond academic exercises
Learn directly from experienced engineers and technical founders
Contribute to projects across energy, manufacturing, mining, and critical infrastructure
Develop your skills across applied machine learning, data engineering, and production AI
Take ownership of meaningful technical work early in your career
What we offer
Competitive compensation
Opportunity to participate in an Employee Stock Option Plan (ESOP)
Mentorship and direct collaboration with experienced AI engineers
Hands‐on exposure to the full AI development lifecycle
Small, sharp team with no unnecessary layers or bureaucracy
Opportunities for technical growth, research, and increasing ownership
Sounds like you?
We welcome applications from recent graduates and early‐career candidates with strong technical potential.
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📌 Early Data Scientist (Hybrid) (Melbourne)
🏢 Syncrowin Software Solutions Pty
📍 Melbourne