This position is offered through the ANU Computing Internship courses (COMP3820 / COMP4820 / COMP8830).
Semester 2, 2023 applications open on Tuesday 23 May and close on Monday 29 May.
UnicornShift is a digital marketplace for civil infrastructure services.
Our company operates a digital marketplace for civil infrastructure services, connecting clients with a wide range of service providers. As part of our continuous efforts to improve our platform, we are looking to develop a recommendation engine to better match clients with suitable service providers based on their project needs and preferences.
Project 1—Historical data analysis
In Project 1 you will analyse historical data. This involves retrieving the data from the platform and analysing it.
The primary objective of this project is to design and implement a recommendation engine that will improve the user experience on our platform by suggesting relevant service providers to clients based on their project requirements and preferences. The recommendation engine should be able to:
- Analyse historical data of completed projects and successful client-service provider matches.
- Identify key factors that contribute to successful project outcomes and client satisfaction.
- Generate personalized recommendations for clients based on their project requirements and preferences.
The project will be carried out over the course of a semester, with a total of 180 hours of effort. The scope of the project includes the following tasks:
Data Collection and Preprocessing: Gather historical data from our platform, clean and preprocess the data for analysis. This may involve removing outliers, handling missing values, and encoding categorical variables.
Exploratory Data Analysis (EDA): Perform EDA to identify trends, patterns, and relationships among variables that could be relevant for generating recommendations.
Feature Engineering: Based on the insights from the EDA, create new features that could be relevant for the recommendation engine.
Model Development: Design and implement a recommendation engine using machine learning algorithms such as collaborative filtering, content-based filtering, or a hybrid approach.
Model Evaluation: Assess the performance of the recommendation engine using relevant evaluation metrics, such as precision, recall, and F1-score.
Model Optimization: Fine-tune the model to improve its performance based on the evaluation results.
Integration: Integrate the recommendation engine into our digital marketplace platform using APIs or other relevant technologies.
Documentation: Document the entire process, including design decisions, implementation details, and evaluation results.
Cleaned and preprocessed historical data set.
EDA report with visualizations and insights.
Recommendation engine implemented using a suitable machine learning algorithm.
Model evaluation report with performance metrics.
Integration of the recommendation engine into the digital marketplace platform.
Comprehensive project documentation.
By the end of the semester, the interns should have developed a functional recommendation engine that can be integrated into our digital marketplace platform, improving the user experience and increasing the chances of successful client-service provider matches.
Required technical skills
Required/preferred professional and other skills
Hybrid (Project can be undertaken in-person or remote)
We are happy to host a student in any location
Project’s Special Requirements/ Conditions
Type of internship
The intern/s will be engaged as a casual employee
How to apply
Applications are invited from eligible students to apply for the Computing Internship courses COMP3820 or COMP4820 or COMP8830. Eligibility details of COMP3820 / COMP4820 / COMP8830 and further information about the Computing Internship can be found on the Computing Internship page.
Eligible students can apply through the Computing Internship application form which will be available via the Computing Internship page between Tuesday 23 May 2023 to Monday 29 May 2023.
You can nominate multiple preferred Internship projects and host organisations through the one online application form.
Eligibility and Room Available in degree to undertake COMP3820/COMP4820/COMP8830 will be assessed at the time of application. If you do not meet the eligibility criteria or do not have room in your degree to fit COMP3820/COMP4820/COMP8830, your application will not be progressed.
Your application will require you to upload the following documents:
- an updated copy of your Resume, and
- an Expression of Interest (limit 350 words) for each organisation you wish to apply to (for organisations with multiple projects only submit one Expression of Interest but state clearly which project/s you wish to be considered for).