Engineering Data Scientist Intern

ROLE AND RESPONSIBILITIES
Assist in the development and improvement of internal data science tools, solutions and systems used by Veriphi in its development of laser intravenous drug analysis solutions. Tackle technical challenges, provide suggestions, and participate in group problem solving. Support the development, improvement and set up of testing environments and solutions.

This is a fixed term contract, commencing 11 November 2025 for a total of 400 hours. Work schedule will be 40 hours per week, with a holiday shutdown of 2 weeks over the Christmas/New Year holiday period.

i. Veriphi’s proprietary products utilize Machine Learning technology. Various methods to improve model performance need to be investigated. This may provide the following:
• Performance improvements
• Flexibility
• Scalability
• New features
• New functionality
This will involve working with and understanding the relationships between data, electronics, software, firmware, mechanical, and associated design, development & testing tools

ii. Project – Investigate machine learning techniques to improve predictive model performance:
• Investigate techniques to improve the performance of machine learning models, such as feature engineering, feature selection, and AutoML.
• Apply appropriate statistical tests to evaluate the performance of machine learning models.
• Integrate any improvements into existing deployment architecture with required documentation, unit-testing, and CI/CD pipelines.
• Test for correct operation, including integration with supporting firmware and software solutions.
• Document testing methods, steps, and results.

KEY ATTRIBUTES
• Strong Python programming skills (intermediate level), confident writing, debugging, and optimizing code.
• Familiarity with key AI/ML Python libraries: pandas, NumPy, and scikit-learn are a must. Experience with visualization libraries like matplotlib and seaborn is also required.
• Good understanding of core data science concepts: supervised learning, classification and regression models, cross-validation, and model evaluation using statistical tests.
• Hands-on, proactive, and self-motivated.
• Able to work independently or in a team environment.
• Strong communication and reporting skills.