Data Scientist at Keypath Education

Melbourne, Victoria, Australia -

Full Time

Start Date

Immediate

Expiry Date

12 Oct, 26

Salary

0.0

Posted On

14 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Generative AI, Predictive Modelling, LLMs, RAG Pipelines, Prompt Engineering, Statistics, MLOps, Feature Engineering, Azure AI Foundry, LangChain, Data Visualization, Model Monitoring, Stakeholder Management, People Management

Industry

Higher Education

Description

Make an Impact with Data That Drives Global Education Keypath Education is a leading EdTech company that partners with universities to design, launch, and manage high-quality online degree programs. We combine data-driven decision-making with deep sector expertise to help our university partners grow enrolment, improve student outcomes, and expand access to education. Why Join Keypath? Global EdTech leader across Australia and SE Asia Flexible “Work Anywhere” model (remote, hybrid or office) High-growth environment with strong career development opportunities Collaborative, innovative, people-first culture Certified as a Great Place to Work in Australia & Malaysia The Opportunity We are looking for a mid-level Data Scientist to join our Strategy & Insights team. Reporting directly to the Director of Data, Insights & AI, you will design and deliver predictive models and generative AI solutions that drive commercial outcomes across the student lifecycle. You will work across the full model development lifecycle—from problem framing and exploratory analysis through to deployment and monitoring—and collaborate closely with stakeholders across marketing, enrolment, student success, and product teams. What You’ll Be Doing Design, build, and validate predictive models (e.g. lead scoring, propensity modelling, forecasting, risk prediction) that inform operational and strategic decisions. Develop and deploy generative AI solutions, including LLM-powered agents, retrieval-augmented generation (RAG) pipelines, and prompt engineering workflows to automate and enhance business processes. Translate business problems into well-scoped analytical and modelling projects, working with stakeholders to define success criteria and measurable outcomes. Perform feature engineering, model selection, hyperparameter tuning, and rigorous evaluation using appropriate statistical and ML techniques. Productionise models into scalable, maintainable pipelines, collaborating with data engineering to integrate outputs into downstream systems (e.g. CRM, BI tools, operational dashboards). Monitor model performance post-deployment, manage model drift, and implement retraining strategies. Communicate findings and model outputs to non-technical stakeholders through clear visualisations, written summaries, and presentations. Stay current with developments in applied ML and generative AI, and contribute to the team’s knowledge-sharing and capability-building efforts. Provide guidance, direction, and oversight to one direct report within the Data, Insights & AI team. About You Bachelor’s degree or Master’s degree in data science, statistics, computer science, mathematics, engineering, economics, or a related quantitative field. 3–5 years of professional experience in a data science, machine learning, or applied analytics role. Experience directly managing or mentoring one or more analysts, including providing technical guidance, performance feedback, and professional development support. Strong proficiency in Python for data science (pandas, scikit-learn, XGBoost/LightGBM, statsmodels, or equivalent). Hands-on experience building and deploying predictive models in a commercial or operational context (not solely academic/Kaggle). Practical experience with large language models (LLMs), including prompt engineering, fine-tuning, or building agentic AI workflows using frameworks such as LangChain, Semantic Kernel, or Azure AI Foundry. Solid grounding in statistics and experimental design (hypothesis testing, regression, classification, time series). Experience with MLOps practices: model versioning, CI/CD for ML pipelines, monitoring, and reproducibility. Strong communication skills with the ability to present complex technical work to senior business stakeholders in a clear, outcome-focused manner. Additional Skills & Experience that are desirable but not essential include the following: Experience with Microsoft Azure cloud services (Azure ML, Azure AI Foundry, Azure OpenAI Service). Familiarity with Microsoft Fabric, Databricks, or similar modern data platforms. Experience with MLflow or equivalent experiment tracking and model registry tools. Exposure to the education, EdTech, or student lifecycle domain. Experience integrating model outputs into CRM platforms (e.g. Dynamics 365, Salesforce). Familiarity with R for statistical modelling. What We Offer A high-impact role in a growing Data, Insights & AI function with executive visibility. The opportunity to work across both predictive ML and cutting-edge generative AI in a commercially grounded setting. A collaborative, low-ego team culture that values learning, curiosity, and pragmatic delivery. Professional development support, including access to certifications and training programmes. Flexible working (remote, hybrid or office) Employee Assistance Program and wellbeing initiatives Access to LinkedIn Learning and career development programs IT Equipment provided for your success Ready to Make an Impact? Join Keypath and help shape the future of education. We welcome applicants from all backgrounds and are committed to an inclusive hiring experience. Apply now and unlock your potential with Keypath. Please note that only candidates residing in Australia with full working rights will be eligible for consideration. Preferred applicants may be required to complete a police check and other pre-employment checks.

Responsibilities

Design and deploy predictive models and generative AI solutions to drive commercial outcomes across the student lifecycle. Collaborate with cross-functional teams to translate business problems into analytical projects and provide oversight to a direct report.