Were looking for a data scientist to join our technology team. You’ll leverage cutting-edge cloud technology and work on exciting challenges that directly impact business decisions and user experiences. Machine learning & automation Design, build, test and optimise predictive models that deliver automated business intelligence Develop sophisticated algorithms to solve complex business challenges Deploy and monitor ML models in production environments with proper versioning and tracking MLOps & model lifecycle management Track model performance, detect drift and manage model retraining workflows Containerise ML applications and manage model versioning across environments Partner with cross-functional stakeholders to identify, scope and solve critical business problems Create automated reporting systems and interactive dashboards that empower data-driven decision making Monitor platform performance and establish key performance metrics Analytics & insights Analyse diverse data sources including custom analytics, paywall metrics, and web analytics to uncover actionable business insights Conduct deep-dive user behaviour analysis to enhance UX and drive engagement Data engineering & pipeline development Build and maintain robust data pipelines for ingesting, processing and transforming large datasets Ensure data quality and implement validation checks across data workflows Design efficient ETL/ELT processes to support analytics and ML initiatives Education & Experience Honours degree (minimum) in Data Science, Mathematics, Statistics, Engineering or related field 3 years of hands-on Python development experience Proven experience with big data technologies and cloud platforms Technical expertise Development Tools: Git version control, Jupyter Notebooks, Docker ML Frameworks: scikit-learn, PyTorch, TensorFlow, LightGBM, XGBoost, Pandas Data Engineering: SQL, DAG orchestration tools, data pipeline design, ETL/ELT processes Statistical Methods: Linear/logistic regression, statistical analysis techniques Recommendation Systems: Collaborative filtering, content-based and hybrid models Tree-Based Methods: Random Forests, decision trees, gradient boosting Advanced Techniques: Clustering algorithms, Natural Language Processing (bag-of-words, word embeddings, transformer models) Model Deployment: Production deployment, A/B testing, model monitoring and maintenance Data engineering skills Strong SQL proficiency and database design principles Experience with data warehousing concepts and dimensional modelling Knowledge of data quality frameworks and validation processes Understanding of streaming vs. batch processing architectures Mindset Curiosity and eagerness to learn emerging technologies, platforms and methodologies Problem-solving approach with attention to detail and business impact Strong collaboration skills for working across engineering and business teams

Key Responsibilities: Explore and analyse large datasets to uncover insights that drive business value. Build, refine, and deploy predictive models to improve credit and capital strategies. Leverage machine learning and AI tools to enhance efficiency and innovation. Work closely with business teams to translate data into actionable results. Job Experience and Skills Required: Degree in Data Science, Mathematics, Actuarial Science, Statistics, or another quantitative field. Up to 1 year in a data-focused role (bonus points if its in fintech, credit, or financial services). Proficient in Python, R, and SQL, with an understanding of machine learning and AI techniques. Analytical thinker with a curious streak, eager to learn and make an impact from day one. Apply now!