Data Scientist

Keypath Education

Data Scientist driving predictive models and generative AI solutions that drive commercial outcomes across the student lifecycle at Keypath Education. Collaborating with various teams to enhance business processes and improve student outcomes.

Posted 7/14/2026full-timeRemote • 🇦🇺 AustraliaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying predictive models and generative AI solutions, with a strong foundation in statistics and machine learning techniques. Capable of translating complex business problems into actionable analytical projects while effectively communicating findings to stakeholders.

Highest-signal resume keywords

Predictive Model DevelopmentGenerative AI SolutionsPython ProficiencyMLOps PracticesStatistical Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModellingFeature EngineeringHyperparameter TuningModel EvaluationLarge Language ModelsPrompt EngineeringCI/CD for ML PipelinesStatistical TechniquesData ScienceMachine Learning

Soft Skills

Communication SkillsMentoringStakeholder Engagement

Tools & Technologies

PandasScikit-LearnXGBoostLightGBMStatsmodelsLangChainSemantic KernelAzure AI FoundryCRM SystemsBI Tools

Industry Keywords

Data ScienceMachine LearningApplied AnalyticsModel DriftExperimental Design

Tech Stack

Tools & technologies

AzurePandasPythonScikit-Learn

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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