We are looking for a
Data Scientist who can take full ownership of high-impact, data-driven initiatives across product, learner, and business domains. This role involves advanced analytics, modelling, automation, and production deployment, along with mentoring junior team members and influencing strategic decisions.
You will work closely with product, academic, engineering, and operations teams to convert complex problems into scalable, reliable, and measurable data science solutions.
Key Responsibilities
- Lead end-to-end data science projectsfrom problem framing and hypothesis design to modelling, deployment, monitoring, and optimisation.
- Analyse product, learner, and operational data to identify performance gaps, behavioural patterns, and growth opportunities.
- Build and deploy advanced analytical and predictive models (forecasting, segmentation, clustering, recommendation logic, etc.).
- Design and implement automations and data-driven enhancements to improve product performance and operational efficiency.
- Ensure production-grade reliability by setting up monitoring, alerting, versioning, and retraining strategies for models and pipelines.
- Translate complex analytical outcomes into clear, actionable insights for leadership and cross-functional stakeholders.
- Collaborate closely with engineering and DevOps teams to integrate models into existing systems and workflows.
- Maintain high standards of data accuracy, integrity, documentation, and reproducibility.
- Mentor and coach junior data scientists; review analyses and models to uphold quality and best practices.
- Continuously evaluate and adopt improved modelling approaches, tools, and techniques.
Skills Required
- Python (NumPy, Pandas, Scikit-learn), SQL; experience working with large, complex datasets
- Statistical analysis, hypothesis testing, regression, classification, clustering, time-series analysis
- Feature engineering, model evaluation, tuning, and validation; ability to choose practical models over theoretical complexity
- Experience building and maintaining data pipelines, automations, and batch/near-real-time workflows
- Deploying models to server environments; familiarity with APIs, model versioning, monitoring, and performance tracking
- Dashboards and reports using tools such as Power BI, Tableau, Looker, or equivalent
- Exposure to AWS / GCP / Azure, Docker, or similar production environments
