Data Science Intern

Xtracta is a technology company focussing on the development of technology that can read and find data within structured and semi-structed documents. The technology is used around the world and spans a large swathe of use cases, languages and users. The company also is working on data analytics and insights products.

Xtracta is seeking a Data Science Intern to join our R&D team on a 2–3 month contract, with the potential to transition into a long-term position.

Role Description

The Data Science Intern will contribute to ongoing research and development initiatives alongside troubleshooting and optimizing ML models. There will be a focus on supporting data collection, dataset preperation, documentation and model training. The role will involve two primary areas of work:

1. Training Dataset Preparation – Gathering and curating training datasets for various labelling projects. This includes understanding project requirements, querying databases for target documents, ensuring data quality, and writing clear documentation to support future use.
2. Extraction Issue Documentation – Reviewing the code base and creating detailed guides on resolving data extraction related issues. This may involve first-hand experience in handling client inquiries about data extraction problems, followed by documenting effective troubleshooting steps and solutions.

Additionally aspects of data analysis may be included in small or large part depending on the aptitude of the intern.

Success in this role will require strong SQL and coding skills, attention to detail, and clear oral and written communication. The intern will gain hands-on exposure to data preparation, technical issue analysis, and cross-functional collaboration in a machine learning and data-driven environment.

Team Support and Learning Opportunities

The intern will be supported by an experienced data and engineering team, providing guidance and mentorship across multiple areas. The team will:

• Foster collaboration – Help the intern learn how cooperation works in IT industry teams, including agile practices, peer reviews, and cross-functional collaboration.
• Solve real-world problems – Provide hands-on experience in tackling practical challenges such as data quality issues, extraction errors, and customer-driven requirements.
• Facilitate stakeholder interaction – Offer opportunities to observe and participate in discussions with internal and external stakeholders, gaining insight into how technical solutions align with business needs.
• Provide tool exposure – Introduce a variety of tools and technologies used in data science and software development, including SQL, programming frameworks, version control systems, and documentation platforms.

This environment will help the intern not only develop technical skills but also build an understanding of how data science fits within real-world IT projects and business contexts.

Requirements and Skills

We are looking for intelligent applicants who demonstrate creative problem-solving abilities and a genuine desire to work on challenging problems. Success in this role will require strong SQL and coding skills, attention to detail, and clear oral and written communication. The intern will also gain hands-on exposure to data preparation, technical issue analysis, and cross-functional collaboration in a machine learning and data-driven environment.

Key skills and qualities sought include:

• Competent programming ability, ideally with Python experience
• Excellent SQL skills, including working with large datasets
• Strong communication skills, both oral and written
• Ability to multi-task, stay organised, and work in a disciplined manner
• Curiosity, creativity, and the drive to tackle difficult problems

Preferred background: Computer science and/or data science

Eligibility:Please note that only New Zealand citizens, residents, or visa holders with suitable working rights are eligible to apply.

In your application please include (these can be placed in your cover letter or provided separately):

• Your résumé
• Your cover letter
• Your most recent academic transcript
• Any links to online projects (that are available), project overview documents or any other information you feel would help us best consider your accomplishments