New opportunity

Data Scientist Lead

Changi Airport Group · Singapore

About this role

The Data Scientist Lead will drive the strategic development and delivery of data and AI products to support intelligent decision-making across airport operations and business functions. Reporting to the Senior Vice President of Airport Operations Technology & Corporate IT, this leadership role bridges business requirements with technical execution. Responsibilities include designing scalable data products, managing a team of data scientists and engineers, and collaborating with cross-functional stakeholders to deliver actionable insights. Key Responsibilities: Collaborate with product owners and data engineers throughout the product lifecycle from concept to deployment. Collect, process, and analyze large-scale datasets. Design, build, and validate machine learning models and statistical algorithms. Perform exploratory data analysis to identify trends. Communicate complex findings to non-technical stakeholders. Qualifications: Minimum 10 years of experience in data science or a similar function, including at least 3 years in a leadership role managing data scientists, ML engineers, and big data specialists. Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field. Technical Proficiency: Strong programming skills in Python or R. Hands-on experience with machine learning libraries such as scikit-learn, XGBoost, TensorFlow, and PyTorch. Expertise in statistical methods including regression, classification, clustering, time series forecasting, and anomaly detection. Familiarity with MLOps practices, including model deployment, monitoring, and versioning using tools like MLflow and Airflow. Experience with data visualization tools (Tableau or Power BI) and version control systems (Git). Knowledge of data governance, privacy, and security practices such as GDPR and PDPA.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonRscikit-learnXGBoostTensorFlowPyTorchMachine LearningStatistical ModelingTime Series ForecastingAnomaly DetectionMLOpsMLflowAirflowCI/CDGitTableauPower BIData WranglingFeature EngineeringData Governance

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Strategic Product Delivery
The role requires driving the end-to-end lifecycle of data and AI products, from concept to deployment and scaling. You must prioritize the creation of reusable, scalable, and cost-effective solutions that directly support airport operations and business decision-making.
Cross-Functional Stakeholder Management
You will act as a bridge between technical teams and non-technical business units. Success depends on your ability to translate complex operational needs into technical requirements and communicate actionable insights effectively to senior leadership and product owners.
Technical Leadership and MLOps Maturity
Beyond individual contribution, you must lead a team of data scientists and engineers. A core priority is establishing robust MLOps practices, including model versioning, deployment, and monitoring using tools like MLflow and Airflow to ensure reliable production performance.

Likely challenges to think through

Balancing Technical Rigor with Operational Speed
Operating in a fast-paced airport environment requires delivering high-quality predictive models while managing multiple concurrent projects. Prepare by demonstrating how you prioritize tasks and maintain model quality without sacrificing deployment speed.
Navigating Complex Data Governance and Privacy
As an airport operator, handling large-scale datasets involves strict adherence to privacy regulations like PDPA and GDPR. Prepare by being ready to discuss how you integrate data governance and security by design into the early stages of the product development lifecycle.

Practice your answers

Technical practice

Designing Scalable Data Products

What to coverExplain your approach to designing reusable and scalable data products within a large organization. Focus on how you ensure architecture supports long-term maintenance, integration with existing data lakes, and alignment with business objectives.

MLOps and Model Lifecycle Management

What to coverDescribe your experience implementing MLOps practices, specifically regarding model deployment, versioning, and monitoring. Discuss how you use tools like MLflow or Airflow to ensure models remain performant and reliable in a production environment.

Statistical Methods for Airport Operations

What to coverGiven the operational nature of an airport, how would you apply time series forecasting or anomaly detection to improve decision-making? Discuss the trade-offs between different algorithms and how you validate their effectiveness.

Data Governance and Privacy

What to coverHow do you incorporate data governance, security, and privacy (such as PDPA or GDPR) into the data product lifecycle? Explain how you balance the need for data accessibility with strict compliance requirements.

Feature Engineering and Model Validation

What to coverDescribe your process for feature engineering when dealing with large-scale, complex datasets. Explain how you define success metrics that are grounded in real business context rather than just statistical performance.

Collaborating with Data Engineering Teams

What to coverHow do you bridge the gap between data science requirements and data engineering infrastructure? Discuss your experience working with cloud architects and engineers to build robust data pipelines.

Exploratory Data Analysis (EDA) Strategy

What to coverWhen presented with a new, ambiguous operational problem, what is your systematic approach to EDA? Explain how you identify trends and translate those initial findings into actionable business insights.

Tooling and Version Control

What to coverExplain your workflow for using Git in a collaborative team environment. Suggestion: Focus on branching strategies, code reviews, and maintaining high-quality documentation for shared codebases.

Behavioural practice

Communicating Complex Findings to Stakeholders

What to coverDescribe a time you had to explain a complex machine learning model or technical finding to a non-technical stakeholder. How did you ensure they understood the business impact and the limitations of the analysis?

Leading and Mentoring Data Teams

What to coverProvide an example of how you have managed a team of data scientists and ML engineers. Focus on how you balanced technical oversight with the need to foster professional growth and team productivity.

Managing Cross-Functional Projects

What to coverDescribe a situation where you had to collaborate with multiple business units to deliver a data product. How did you manage conflicting priorities and ensure the final product met the needs of all stakeholders?

Handling Project Ambiguity and Fast-Paced Environments

What to coverTell me about a time you had to deliver a data solution in a fast-paced environment with shifting requirements. How did you maintain quality and ensure the project stayed on track?

Sharpen your knowledge

Topics to prepare

MLOps Lifecycle Management
Focus on the end-to-end deployment, monitoring, and versioning of machine learning models. Exercise: Draft a strategy for maintaining model performance in production, specifically addressing how you would use MLflow for versioning and Airflow for pipeline orchestration in a high-stakes airport operations environment.
Data Governance and Privacy Compliance
Understand the application of GDPR and PDPA in large-scale data environments. Exercise: Review your past projects to identify how you ensured data privacy during feature engineering and model training, and prepare to discuss how you would implement these standards within a complex corporate IT infrastructure.
Strategic Stakeholder Communication
Bridging the gap between technical execution and business objectives. Exercise: Prepare a case study where you translated complex statistical findings into actionable business insights for non-technical leadership, focusing on how you aligned model success metrics with operational KPIs.

Skills to revise

Time Series Forecasting and Anomaly Detection
Develop proficiency in applying these specific statistical methods to operational data. Exercise: Build a prototype model using Python to detect anomalies in a sample dataset, ensuring you can explain the choice of algorithm and how it addresses specific business trends.
CI/CD and Version Control Integration
Mastering the integration of ML workflows into CI/CD pipelines using Git. Exercise: Create a mock repository structure that demonstrates how you manage code, model artifacts, and documentation to ensure reproducibility and team collaboration.
Data Visualization for Decision Support
Using Tableau or Power BI to present complex data. Exercise: Design a dashboard layout that tracks key performance indicators for airport operations, focusing on how to visualize model outputs to facilitate rapid, intelligent decision-making by non-technical stakeholders.

Your preparation checklist

Prepare a leadership case study
Develop a structured walkthrough of a past project where you managed a team of data scientists and ML engineers to deliver a production-grade AI product. Focus on how you bridged business requirements with technical execution, specifically highlighting your approach to MLOps, model lifecycle management, and stakeholder alignment.
Map technical expertise to airport operations
Review your experience with time series forecasting and anomaly detection. Prepare to discuss how you would apply these specific statistical methods to large-scale airport datasets, such as passenger flow, operational logistics, or predictive maintenance, ensuring your examples demonstrate scalability and business impact.
Draft a data governance and ethics framework
Prepare a brief overview of how you have implemented data privacy and security standards like GDPR or PDPA in previous roles. Be ready to explain how you balance the need for data accessibility with strict governance requirements when collaborating with cross-functional IT and data engineering teams.
Formulate strategic questions for leadership
Prepare insightful questions for the Senior Vice President regarding the current maturity of the organization's data lakes and pipelines, the primary operational bottlenecks the team is currently prioritizing, and how the department measures the ROI of its AI product portfolio.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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