Data Scientist

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Keypath Education

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2014

📚 Education

☁️ SaaS

🤝 B2B

Education • SaaS • B2B

Keypath Education is a global educational technology company that partners with universities to design, market, and deliver career-focused online higher-education programs. It operates an end-to-end technology and data platform (KeypathEDGE) to power program selection, predictive analytics, learning design, student recruitment, and student experience — including clinical and field placement management — serving university partners across the US and Asia-Pacific with particular emphasis on scaling healthcare programs.

📋 Description

• 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.

🎯 Requirements

• 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

🏖️ Benefits

• 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 • 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