Early Data Scientist – Hybrid in Melbourne

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 professional 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 Py Torch or Tensor Flow
• Exposure to cloud platforms, particularly Azure
• Experience with sensor data, Io T, 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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