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
• Strong Python programming skills
• 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

Why join

• 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