Computer Vision / Data Scientist


About the role

If you’re ready for the next challenge in your career, VAPAR wants to hear from you.



  • Work closely with web developers, product owners and the business representatives to manage the implementation of high-quality ML models.

  • Managing the transfer of data between the ML model and a web environment.

  • Optimise ML model outputs using computer vision pre-processing techniques to improve customer outcomes.

  • Optimise dataset cleaning and augmentation for any data added in to ML model training datasets.

  • Retrain and evaluate the ML model to improve and optimise customer outcomes.

  • Continue research in the field of data science and computer vision to ensure that technological knowledge is up to date, and may publish works about research and outcomes.


About you:

  • Experience in writing and utilising sophisticated Computer Vision algorithms (e.g. Open CV) and Deep Learning frameworks (e.g. Tensorflow) for recognition, classification and/or segmentation of custom objects from images and videos.

  • Experience in programming in Python.

  • Familiarity with SQL databases and the Microsoft Azure environment.

  • Experience, or willingness to learn, any of the following: object detection for target tracking in video, simultaneous localisation and mapping (SLAM) and related fields.

  • Ability to demonstrate and/or describe details of previous relevant implementation experience.

  • Effective communication skills relating to project progress and challenges.

  • Full working rights in Australia. 


About the offer:

  • Sydney CBD based location

  • Full-time hours (flexible working arrangements available)

  • Competitive salary and share options based on candidate's ability to demonstrate excellence.

  • Being an integral part of a high growth, fast paced start-up providing world-leading software solutions to engineering firms and utilities.

  • As a small company, strong performers are quickly recognised, rewarded and constantly challenged. There is also a strong focus on learning on the job and working as a team.

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