{"id":3665260,"url":"https://alion.io/job/australian-astronomical-optics-macquarie-data-science-intern","title":"Data Science Intern","company":{"id":2246540,"name":"Australian Astronomical Optics – Macquarie","domain":"aao.org.au","url":"https://alion.io/company/aao-org-au","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"Early Careers","role_family":"Early Careers","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Australia"],"countries":["AU"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CatBoost","optional":false},{"name":"CI/CD","optional":false},{"name":"GitHub","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-11-03T05:49:08Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-04T16:09:31Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"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\nTo 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.\nList of the main tasks/duties\nUnder the guidance and supervision of the Senior Data Analyst:\nAssist in enhancing and testing of existing machine learning models to improve their accuracy, robustness and usefulness for decision-making. \nExtract, prepare and analyse data using SQL and Python, ensuring datasets are accurate, consistent and suitable for modelling in accordance with established standards and guidelines.\nAssist with running test cases, checking how model performance and outputs change under different assumptions and features, to support the team with model refinement.\nAssist in evaluating model performance and validating results to help ensure outputs are robust and defensible. \nDocument experiments, methods, code changes and results in line with established ML Ops framework and documentation standards.\nCommunicate findings and the limitations of each approach to support informed decision-making. \nHandle confidential and sensitive data in accordance with University privacy, data governance and information security requirements. \nComply with relevant EEO and WHS regulations. \nPerform any other duties as required and appropriate for this classification. \nSelection criteria\nCurrent enrolment in, or recent completion of, tertiary studies in data science, computing, statistics, mathematics, actuarial studies or a related field.\nDemonstrated 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. \nUnderstanding of core machine learning concepts, including training and test design, overfitting and model evaluation.\nHigh attention to detail and well-developed analytical and problem-solving skills. \nEffective communication skills (written and verbal) with the ability to work collaboratively with team members and stakeholders, and communicate technical information clearly.\nAbility to exercise discretion and maintain confidentiality when handling sensitive information.\nDesired\nKnowledge or experience with gradient boosting models (for example XGBoost, LightGBM or CatBoost) and model explainability techniques.\nExperience with Snowflake or another cloud data platform.\nVersion control using GitHub.\nUnderstanding of CI/CD concepts as applied to data science or analytics workflows.\nHow to Apply\nTo be considered, please apply online with your CV and a separate cover letter (3-page maximum) outlining how you meet each of the selection criteria (above).\nPlease note that applications for this position are only being accepted from Australian citizens, permanent residents, and those currently residing in Australia with full working rights.\nRole Specific enquiries: Aabha Anand Tiwari \nPre-Employment Checks\nYour employment is conditional upon the completion and maintenance of all role-required pre-employment or background checks in terms satisfactory to the University.\nAbout Us\nMacquarie 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.\nIf 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.\nApplications Close:\n22/11/2026 11:59 PMA Place Where You Belong\nAt 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.\nFlexible Work\nAt 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. 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