Lead Data Scientist

About the Role

We are seeking an experienced Lead Data Scientist to spearhead the strategic creation and implementation of data and AI solutions that facilitate intelligent decision-making across airport operations and various business areas. Reporting to the Senior Vice President of Airport Operations Technology & Corporate IT, this role acts as a bridge between operational/business requirements and technical execution, providing leadership, technical guidance, and stakeholder management to develop scalable, reusable, and cost-efficient data and AI products. These solutions will analyze complex datasets to identify trends, construct predictive models, and deliver actionable insights to enhance airport operations and business performance.

The Lead will collaborate independently or within teams alongside stakeholders from diverse business units, understand their operational and business challenges, and deliver data-driven solutions. This position requires working closely with data engineers, cloud architects, and technology partners to leverage existing data lakes, pipelines, and presentation layers to build organizational capabilities. The candidate should thrive in a dynamic environment managing multiple projects.

Key Responsibilities

• Partner with product owners, data engineers, and cross-functional teams throughout all product development phases, including ideation, design, prototyping, testing, data management, deployment, scaling, and product retirement.
• Aggregate, process, and analyze large-scale datasets from multiple sources.
• Create, construct and validate machine learning models and statistical algorithms.
• Conduct exploratory data analysis to discover insights and emerging trends.
• Work with stakeholders to define clear data requirements and objectives.
• Effectively communicate analyses and recommendations to non-technical audiences.

Required Qualifications and Skills

• A minimum of 10 years’ experience in data science or related roles, including at least 3 years in leading teams of data scientists, machine learning engineers, and big data experts.
• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related discipline.
• Deep understanding of statistical methodologies and machine learning approaches such as regression, classification, clustering, time series forecasting, and anomaly detection.
• Proficient in programming languages like Python and/or R, with hands-on expertise in machine learning frameworks including scikit-learn, XGBoost, TensorFlow, and PyTorch.
• Strong capabilities in data preparation, exploratory data analysis, feature engineering, and model validation; with the ability to establish or select relevant success metrics aligned to business contexts.
• Experience with MLOps practices for model deployment, monitoring, and version control of ML assets using tools such as MLflow, Airflow, and CI/CD pipeline integration.
• Competence in version control systems like Git and collaboration platforms such as GitHub or GitLab.
• Familiarity with data visualization tools including Tableau and Power BI.
• Exceptional problem-solving skills with keen attention to detail.
• Excellent communication, storytelling, and collaborative abilities.
• Knowledge of data governance, security standards, and privacy regulations such as GDPR and PDPA.