Data Scientist

Position: Data Scientist – Optimisation & Applied Mathematics

Location: Brisbane-based | Work-from-Home (fully remote across Australia)

Reports to: Founder & Chief Data Scientist

MAP are delighted to be partnering with an exciting, forward thinking data start up looking to solve real world problems with modern tech innovation. As a key hire, you will be part of the growth and strategy of the business, work directly with the founder, and get client facing experience solving real problems.

Why this role is exciting

• Real-world impact: Use advanced mathematics and optimisation theory to crack hard, high-value problems in property, finance, construction and transport logistics.
• Direct influence: Work side-by-side with the founder; your ideas shape the product roadmap and the culture of a fast-growing, family-run start-up targeting a 10-fold head-count increase.
• Diverse projects, word-of-mouth clients: Tackle everything from forecasting analytics to massive graph-based routing problems for blue-chip and “stealth” clients.

Responsibility

• Design and implement optimisation models (linear, mixed-integer, goal programming) in Python/C++ using solvers such as Gurobi.
• Build, refactor and benchmark code to solve models in near-real-time
• Prototype predictive layers (neural networks, graph neural networks) that feed or warm-start optimisation pipelines.

Essential skills & experience

• Strong mathematical foundation: BSc/MSc/PhD in Mathematics, Operations Research, Computer Science or related field.
• Optimisation expertise: Proven hands-on experience with mixed-integer/linear programming; comfortable coding constraints & objectives from first principles.
• Solver fluency: Gurobi for large-scale optimisation.
• Programming mastery: Python (primary) plus production-grade C++ for performance-critical components.
• End-to-end ownership: Ability to take a problem from client brief through modelling, coding, testing and deployment.

Nice-to-haves

• Neural-network design (PyTorch/TensorFlow), especially for optimisation-aware architectures.
• Applied graph theory for routing, network design or supply-chain problems.
• Industry domain exposure to mining, logistics, property analytics or financial risk.

Soft-skill & culture fit

• Curious problem-solver who enjoys pairing elegant maths with pragmatic engineering.
• Comfortable communicating complex ideas to non-technical executives.
• Thrives in a lean, remote start-up where autonomy and initiative are prized.