IoT Algorithm Engineer: Data Analytics & Modelling

ProFound People; City of Melbourne; Hybrid; AUD 90,000 - 130,000; Full time

Job summary

ProFound People in Melbourne is expanding its engineering function and seeks an Algorithm Engineer to modernise analytical models for our sensor data.

This full‑time hybrid role owns detection and location algorithms, works with historical datasets and collaborates with software to validate and deploy models into production.

Ideal candidates hold a quantitative degree, strong statistical background, and proficiency in Python and MATLAB.

Qualifications

• Strong foundation in statistical modelling and mathematical problem solving, applied to real world, noisy datasets.
• Proficiency in Python and MATLAB for data analysis and algorithm development.
• Experience working on complex, technically demanding analytical problems, ideally involving sensor data.
• Strong communication skills and ability to collaborate with software and engineering teams.

Responsibilities

• Review and rebuild analytical algorithms for detection, location and prioritisation from sensor data.
• Work with historical datasets to identify where models fall short and design improvements.
• Apply statistical and mathematical methods to extract meaningful patterns from large datasets.
• Collaborate with the software engineering team to validate models and support deployment into production.
• Help establish the working practices and documentation of a new data analytics function.
• Communicate findings and model performance to both technical and non-technical stakeholders.

Skills

Statistical modelling

Data analysis

Mathematical problem solving

Communication skills

Education

Degree in mathematics, statistics, physics, engineering or related field

Tools

Python

MATLAB

Job description

ProFound People in Melbourne is expanding its engineering function and seeks an Algorithm Engineer to modernise analytical models for our sensor data.

This full‑time hybrid role owns detection and location algorithms, works with historical datasets and collaborates with software to validate and deploy models into production.

Ideal candidates hold a quantitative degree, strong statistical background, and proficiency in Python and MATLAB.