Data & Applied Science
Data scientists, applied scientists and research engineers who turn data into decisions and models into measurable outcomes.
A$150k – A$260k package
The oldest AI discipline and still the most broadly demanded — but the title has fragmented. "Data scientist" now spans experimentation and causal inference, production ML, and LLM-adjacent applied science, and companies routinely advertise one while needing another.
We scope before we search: which decisions the role owns, what infrastructure exists, and what "good" looks like in twelve months. Then we assess against that — statistical judgement, modelling depth, and the communication skill that separates a scientist who influences decisions from one who produces notebooks.
- Data Scientist (product, decision, experimentation)
- Applied Scientist
- Research Engineer
- Machine Learning Engineer (classical + deep)
Where this hiring goes wrong
Decision science ≠ production ML
Experimentation and causal inference is a different career from shipping models behind an API. The interview loops, the portfolios and the compensation all differ — a spec that asks for both usually gets neither.