About the role
Lead Data Scientist driving the strategic development of data and AI products for airport operations and business functions. The role involves managing a technical team to deliver scalable predictive models and actionable insights through cross-functional collaboration with engineering and cloud architecture teams.
AviationOnsite6884
Key Responsibilities
- Provide technical strategies and team leadership to design and develop reusable, scalable, and cost-effective data and AI products
- Collaborate with product owners, data engineers, and partners across all product development stages from concept and design to deployment
- 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
- Collaborate with stakeholders to define data requirements and objectives
- Communicate findings and recommendations to non-technical stakeholders
- Collaborate with data engineering and cloud architect teams to leverage data lakes, pipelines, and presentation layers
Requirements
- At least 10 years of experience in a proven role as a data scientist or similar function
- At least 3 years in 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 knowledge of statistical methods and machine learning techniques such as regression, classification, clustering, time series forecasting, and anomaly detection
- Strong programming skills in Python (and/or R), with hands-on experience using machine learning libraries such as scikit-learn, XGBoost, TensorFlow, and PyTorch
- Solid understanding of data wrangling, exploratory data analysis, feature engineering, and model evaluation
- Ability to select or define appropriate success metrics grounded in real business context
- Familiarity with MLOps practices including model deployment, monitoring, and versioning of ML artifacts (e.g., MLflow, Airflow, integration with CI/CD pipelines)
- Experience using Git for version control and familiarity with common collaboration tools such as GitHub or GitLab
- Experience with data visualization tools like Tableau or Power BI
- Excellent problem-solving skills and attention to detail
- Strong storytelling, communication and collaboration skills
- Awareness of data governance, privacy, and security practices (e.g., GDPR, PDPA)