About the role
Responsibilities:
- Build predictive models for learner outcomes: dropout risk, knowledge gaps, learning pace, and content recommendation.
- Design and analyse A/B experiments with rigorous statistical methodology and business metric alignment.
- Analyse large-scale learner interaction datasets to surface actionable patterns for product and curriculum teams.
- Develop and ship ML solutions end-to-end: from exploration to serialised models, API wrappers, and monitoring.
- Build dashboards and data narratives that communicate findings clearly to non-technical stakeholders.
- Partner with engineering on feature store design and pipeline automation for recurring model inputs.
- Run causal inference analyses to attribute learner outcomes to specific interventions or content changes.
- Document model assumptions, evaluation results, and limitations clearly for cross-functional review.
- Leverage cloud platforms and data pipelines to build, deploy, and monitor ML models in production environments.
- Build data visualisations and analytical reports that communicate findings clearly to non-technical product and government stakeholders.
Requirements:
- Strong knowledge of machine learning, statistics, and data analysis.
- Proficiency in Python, SQL, and data science libraries.
- Experience with data preprocessing, feature engineering, and model development.
- Knowledge of predictive analytics, AI/ML models, and model evaluation.
- Experience working with large datasets and data visualisation tools.
- Understanding of cloud platforms and data pipelines is preferred.
- Strong analytical, problem-solving, and communication skills.
Preferred Skills:
- MLOps basics: model serialisation, deployment patterns, monitoring and drift detection.
- Deep learning fundamentals: when to apply neural approaches vs classical ML.
- GenAI applications: LLM-based classification, semantic similarity, and ranking for data science tasks.
- Experimentation platform design: building and maintaining self-serve A/B testing infrastructure.
- Business analytics: funnel analysis, retention modelling, cohort analysis, LTV estimation.