Data Scientist Lead

About the role

An experienced Lead is sought to drive the strategic development and delivery of data and AI products that support intelligent decisions across airport operations and business functions. Reporting to the Senior Vice President, Airport Operations Technology & Corporate IT, the role bridges operations and business needs with technical solutions, providing strategy, stakeholder engagement and team leadership. The candidate works individually and with teams with stakeholders across business units, collaborates with the data engineering, cloud architect and technology partner teams, and works in a fast-paced environment on multiple projects.

Key responsibilities

• Work with product owners, data engineers and partners through all product stages: concept, design, prototyping, testing, data curation, deployment, scaling and end-of-life.
• Collect, process and analyse large data sets from various sources.
• Design, build and validate machine learning models and statistical algorithms.
• Perform exploratory data analysis to uncover insights and trends.
• Define data requirements and objectives with stakeholders and communicate findings to non-technical audiences.
• Use existing data lakes, pipelines and presentation layers to build capabilities for the organisation.

Required profile

• At least 10 years in a proven role as a data scientist or similar, including at least 3 years managing a team of data scientists, machine learning engineers and big data specialists.
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics or a related field.
• Strong problem-solving, storytelling, communication and collaboration skills.
• Awareness of data governance, privacy and security practices (e.g. GDPR, PDPA).

Required skills

• Statistical methods and machine learning: regression, classification, clustering, time series forecasting, anomaly detection.
• Python (and/or R) with scikit-learn, XGBoost, TensorFlow and PyTorch.
• Data wrangling, EDA, feature engineering and model evaluation.
• MLOps: model deployment, monitoring and versioning (MLflow, Airflow, CI/CD).
• Git, GitHub or GitLab, and data visualisation tools such as Tableau or Power BI.