Data Science Intern

Macquarie University; New South Wales; On-site; AUD 55,000 - 75,000; Full time

Job summary

Macquarie University in New South Wales invites a current tertiary education student or recent graduate to gain practical work experience in applying data science and ML to institutional data. You will assist the Business Intelligence & Reporting unit with enhancing, testing and analysing predictive models to generate insights for decision-making and improving student outcomes.

Under guidance of the Senior Data Analyst, you will extract, prepare and analyse data with SQL and Python, run test

Qualifications

• Current enrolment in, or recent completion of, tertiary studies in data science, computing, statistics, mathematics, actuarial studies or a related field.
• Demonstrated knowledge of, and experience using, Python for data analysis and modelling (for example pandas, NumPy, scikit-learn), and SQL for querying and joining relational data.
• Understanding of core machine learning concepts, including training and test design, overfitting and model evaluation.
• High attention to detail and well-developed analytical and problem-solving skills.
• Effective communication skills (written and verbal) with the ability to work collaboratively with team members and stakeholders, and communicate technical information clearly.
• Ability to exercise discretion and maintain confidentiality when handling sensitive information.
• Knowledge or experience with gradient boosting models (for example XGBoost, LightGBM or CatBoost) and model explainability techniques.
• Experience with Snowflake or another cloud data platform.
• Version control using GitHub.
• Understanding of CI/CD concepts as applied to data science or analytics workflows.

Responsibilities

• Assist in enhancing and testing existing machine learning models to improve their accuracy, robustness and usefulness for decision-making.
• Extract, prepare and analyse data using SQL and Python, ensuring datasets are accurate, consistent and suitable for modelling in accordance with established standards and guidelines.
• Assist with running test cases, checking how model performance and outputs change under different assumptions and features, to support the team with model refinement.
• Assist in evaluating model performance and validating results to help ensure outputs are robust and defensible.
• Document experiments, methods, code changes and results in line with established ML Ops framework and documentation standards.
• Communicate findings and the limitations of each approach to support informed decision-making.

Skills

Python for data analysis

SQL querying

Machine learning concepts

Communication skills

Data governance / privacy awareness

CI/CD knowledge for DS workflows

Education

Current enrolment in data science / computing / statistics / mathematics / actuarial studies

Tools

Snowflake

GitHub

Job description

Macquarie University – Macquarie University NSW

To provide a current tertiary education student or recent graduate with practical work experience in applying data science and machine learning techniques to institutional data. The role will assist BIR with the enhancement, testing and analysis of predictive models, including scenario modelling, to generate insights that inform decision-making and improve student support and outcomes.

The Role

To provide a current tertiary education student or recent graduate with practical work experience in applying data science and machine learning techniques to institutional data. The role will assist BIR with the enhancement, testing and analysis of predictive models, including scenario modelling, to generate insights that inform decision-making and improve student support and outcomes.

List of the main tasks/duties

Under the guidance and supervision of the Senior Data Analyst:

• Assist in enhancing and testing of existing machine learning models to improve their accuracy, robustness and usefulness for decision-making.
• Extract, prepare and analyse data using SQL and Python, ensuring datasets are accurate, consistent and suitable for modelling in accordance with established standards and guidelines.
• Assist with running test cases, checking how model performance and outputs change under different assumptions and features, to support the team with model refinement.
• Assist in evaluating model performance and validating results to help ensure outputs are robust and defensible.
• Document experiments, methods, code changes and results in line with established ML Ops framework and documentation standards.
• Communicate findings and the limitations of each approach to support informed decision-making.
• Handle confidential and sensitive data in accordance with University privacy, data governance and information security requirements.
• Comply with relevant EEO and WHS regulations.
• Perform any other duties as required and appropriate for this classification.

Selection criteria

• Current enrolment in, or recent completion of, tertiary studies in data science, computing, statistics, mathematics, actuarial studies or a related field.
• Demonstrated knowledge of, and experience using, Python for data analysis and modelling (for example pandas, NumPy, scikit-learn), and SQL for querying and joining relational data.
• Understanding of core machine learning concepts, including training and test design, overfitting and model evaluation.
• High attention to detail and well-developed analytical and problem-solving skills.
• Effective communication skills (written and verbal) with the ability to work collaboratively with team members and stakeholders, and communicate technical information clearly.
• Ability to exercise discretion and maintain confidentiality when handling sensitive information.
• Knowledge or experience with gradient boosting models (for example XGBoost, LightGBM or CatBoost) and model explainability techniques.
• Experience with Snowflake or another cloud data platform.
• Version control using GitHub.
• Understanding of CI/CD concepts as applied to data science or analytics workflows.

Role Specific enquiries: Aabha Anand Tiwari***************@mq.edu.au

Pre-Employment Checks

Your employment is conditional upon the completion and maintenance of all role-required pre-employment or background checks in terms satisfactory to the University.

About Us

Macquarie is a university engaged with the real and often complex problems and opportunities that define our lives. Celebrating its 60th year in 2024, it was established as a bold experiment in higher education and designed to break from educational traditions to work in tandem with industry. Institutionally we strive for the extraordinary by challenging convention and embracing different views. Our pioneering culture has encouraged and inspired students, staff and collaborators to break free from conventional thinking, and to actively shape the complex issues that define the future of humanity.

If you're already part of the Macquarie Group (MQ University, U@MQ, MQ Health, MGSM), you'll need to apply through your employee Workday account. To apply for this job: Login to Workday and go to the Careers App > Find Jobs.

Applications Close:

22/11/2026 11:59 PM

To provide a current tertiary education student or recent graduate with practical work experience in applying data science and machine learning techniques to institutional data. The role will assist BIR with the enhancement, testing and analysis of predictive models, including scenario modelling, to generate insights that inform decision-making and improve student support and outcomes.

The Role

To provide a current tertiary education student or recent graduate with practical work experience in applying data science and machine learning techniques to institutional data. The role will assist BIR with the enhancement, testing and analysis of predictive models, including scenario modelling, to generate insights that inform decision-making and improve student support and outcomes.

List of the main tasks/duties

Under the guidance and supervision of the Senior Data Analyst:

• Assist in enhancing and testing of existing machine learning models to improve their accuracy, robustness and usefulness for decision-making.

• Extract, prepare and analyse data using SQL and Python, ensuring datasets are accurate, consistent and suitable for modelling in accordance with established standards and guidelines.

• Assist with running test cases, checking how model performance and outputs change under different assumptions and features, to support the team with model refinement.

• Assist in evaluating model performance and validating results to help ensure outputs are robust and defensible.

• Document experiments, methods, code changes and results in line with established ML Ops framework and documentation standards.

• Communicate findings and the limitations of each approach to support informed decision-making.

• Handle confidential and sensitive data in accordance with University privacy, data governance and information security requirements.

• Comply with relevant EEO and WHS regulations.

• Perform any other duties as required and appropriate for this classification.

Selection criteria

• Current enrolment in, or recent completion of, tertiary studies in data science, computing, statistics, mathematics, actuarial studies or a related field.
• Demonstrated knowledge of, and experience using, Python for data analysis and modelling (for example pandas, NumPy, scikit-learn), and SQL for querying and joining relational data.

• Understanding of core machine learning concepts, including training and test design, overfitting and model evaluation.

• High attention to detail and well-developed analytical and problem-solving skills.

• Effective communication skills (written and verbal) with the ability to work collaboratively with team members and stakeholders, and communicate technical information clearly.
• Ability to exercise discretion and maintain confidentiality when handling sensitive information.

Desired

• Knowledge or experience with gradient boosting models (for example XGBoost, LightGBM or CatBoost) and model explainability techniques.
• Experience with Snowflake or another cloud data platform.
• Version control using GitHub.
• Understanding of CI/CD concepts as applied to data science or analytics workflows.

Role Specific enquiries: Aabha Anand Tiwari***************@mq.edu.au

Pre-Employment Checks

Your employment is conditional upon the completion and maintenance of all role-required pre-employment or background checks in terms satisfactory to the University.

About Us

Macquarie is a university engaged with the real and often complex problems and opportunities that define our lives. Celebrating its 60th year in 2024, it was established as a bold experiment in higher education and designed to break from educational traditions to work in tandem with industry. Institutionally we strive for the extraordinary by challenging convention and embracing different views. Our pioneering culture has encouraged and inspired students, staff and collaborators to break free from conventional thinking, and to actively shape the complex issues that define the future of humanity.

If you're already part of the Macquarie Group (MQ University, U@MQ, MQ Health, MGSM), you'll need to apply through your employee Workday account. To apply for this job: Login to Workday and go to the Careers App > Find Jobs.

Applications Close:

22/11/2026 11:59 PM

A Place Where You Belong

At Macquarie, we believe diversity makes us stronger, inclusion drives our success, and belonging inspires us to do our best work. We are proud to foster a community where different backgrounds, identities, and experiences are valued, and where our people are empowered to thrive through supportive leadership, shared responsibility, and a deep commitment to genuine care and respect for our community. Find out more about our vision for a truly inclusive workplace in our Diversity, Inclusion and Belonging Framework.

Flexible Work

At Macquarie, we believe by providing flexibility in when, where and how work is done, we can support our staff to manage their personal commitments, while optimising their work performance and contributions to the University. See how we lead in flexible work to enable an outcome focused and inclusive workplace. To learn more about our culture and hiring process, visit our Jobs at Macquarie page.