Role description:
Enhance the intelligence capability of the organisation to make insights driven decisions by organising, analysing, modelling, interpreting complex data sets and building data products through the innovative use of Artificial Intelligence and Machine Learning.
Key responsibilities:
•Can quickly grasp complex business problems and understand how to leverage data and apply Artificial Intelligence and Machine Learning to support in solving the required challenge.
•Lead the design and delivery of complex data science projects from ideation through to deployment, monitoring and support.
•Develop and deploy sophisticated machine learning, statistical and AI models using scalable cloud-based infrastructure (e.g., Microsoft Azure Machine Learning (ML), Databricks).
•Ensure models are explainable, ethical, and aligned with regulatory and business standards.
•Own the full lifecycle of models, including monitoring, retraining, and performance optimisation.
•Establish and enforce best practices for model governance, versioning and documentation.
•Collaborate with data engineers to design robust, scalable data pipelines and ensure data quality and availability.
•Lead code reviews, knowledge-sharing sessions, and contribute to team capability development.
•Manage project timelines, risks, and dependencies, ensuring high-quality delivery in a fast-paced agile environment.
Key skills/knowledge/experience:
•BSc minimum, MSc or PhD in a STEM field (e.g., Data Science, Computer Science, Mathematics, Statistics, Artificial Intelligence) strongly preferred.
•3–5+ years of professional experience in data science or a related field, with a proven track record of delivering impactful solutions.
•Deep understanding of CRISP-DM, ML Ops, Agile delivery, and ITIL or similar service management frameworks.
•Recognized as a “Thought Leader” in the field of artificial intelligence and machine learning.
Core Technical Skills
•Expert-level proficiency in Python and SQL; experience with software engineering practices such as modular design, testing, and CI/CD.
•Deep expertise in a wide range of ML techniques (e.g., ensemble methods, NLP, time-series forecasting, deep learning); familiarity with model interpretability and fairness tools.
•Strong experience with Microsoft Azure architecture (Azure ML, Azure Synapse), and containerization tools (e.g., Docker, Kubernetes).
•Advanced knowledge of statistical modelling, causal inference, and experimental design (e.g., A/B testing).
•Ability to craft compelling data narratives using Power BI
•Familiarity with ML flow, or similar tools for model tracking and reproducibility.
Good to Have
•Domain knowledge. Understanding of the water industry.
•Collaborate with customers and stakeholders.
•Grow your career, while being exposed to new technologies.
•Lead projects and inspire both colleagues and stakeholders.
•Mentor junior employees using your expertise