Data Scientist Lead
4 days ago
We are looking for an experienced Lead to drive the strategic development and delivery of data and AI products that power intelligent decisions across airport operations and business functions. Reporting to the Senior Vice President, Airport Operations Technology & Corporate IT, this role bridges operations and business needs with technical solutions—providing technical strategies, stakeholder engagement, and team leadership to design and develop reusable, scalable, and cost-effective data and AI products to analyse complex data sets to identify trends, develop predictive models, and provide actionable insights for our airport operations and business. You will be expected to work as an individual and/or a team with stakeholders from various line of business units to understand operation and business needs and deliver data-driven solutions. You will collaborate with our existing data engineering team, cloud architect team and technology partners to leverage our current data lakes, data pipelines and data presentation layers to develop capabilities for the organisation. The candidate is expected to work in a fast-paced environment and participate in multiple projects. Key Responsibilities: Collaborate with product owners, data engineers, and partners across all product development stages - from concept and design to prototyping, testing, data curation, deployment, industry 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. Collaborate with stakeholders to define data requirements and objectives. Communicate findings and recommendations to non-technical stakeholders. Qualifications: At least 10 years of experience in a proven role as a data scientist or similar function, with 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, paired with the 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). #J-18808-Ljbffr
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