Data Scientist

[What the role is]

The Data Scientist will collaborate with multidisciplinary teams, business stakeholders and external partners to apply advanced analytics, statistical methods and machine learning techniques to address complex business challenges and support data-driven decision-making. The role involves translating business needs into analytical solutions, developing and evaluating statistical and machine learning models, and working with relevant technical teams to operationalise these solutions within existing analytics platforms and production environments. These efforts will enhance STB's analytical capabilities and deepen our understanding of visitors to support STB's strategic objectives and contribute to the growth of Singapore's tourism industry.

[What you will be working on]

Main Responsibilities:

Application of Advanced Analytics and Data Science to Support Strategic Business Objectives Translate complex business problems into well-defined data science objectives and determine appropriate statistical and machine learning approaches to address them. Design, develop and implement statistical and machine learning models to address business needs, applying advanced data science techniques where appropriate. Develop and apply robust model validation and evaluation methods to assess model performance, accuracy, reliability and limitations, ensuring that models are fit for their intended purpose. Perform data exploration, preprocessing and feature engineering to prepare complex datasets for model development, training and evaluation. Model Operationalisation and Continuous Improvement Collaborate with data engineers and relevant technical teams to integrate and deploy statistical and machine learning models into production environments, ensuring that they meet operational and business requirements. Monitor and evaluate model performance over time, identify potential performance degradation and recommend appropriate enhancements, recalibration or retraining to maintain model effectiveness. Develop and maintain documentation covering model methodologies, assumptions, validation results and operational requirements to support reproducibility, knowledge transfer and ongoing maintenance. Project Management Manage and take ownership of data science projects, ensuring that deliverables meet agreed objectives, scope, quality standards and timelines, while proactively managing risks, dependencies and stakeholder expectations. Collaborate with cross-functional teams, including data engineers, DevOps engineers, product managers, business analysts and business stakeholders, to support the development, integration and operationalisation of data science solutions. Data Collection and Management Collaborate with relevant teams and stakeholders to review and enhance existing data collection methods and strategies to better support data science and modelling requirements. Evaluate new data sources, including the quality and suitability of datasets and labelled training data, to identify opportunities for enhancing existing data science capabilities. Work with internal and external stakeholders to identify, source and assemble complex datasets that meet the requirements of data science projects. Provide inputs to the design and development of integrated data models to support the use of multiple structured and unstructured datasets for data science applications.

[What we are looking for]

At least 3–5 years of relevant experience in developing, evaluating and implementing statistical and machine learning models to address real-world business problems. Demonstrated ability to independently manage data science projects, engage internal and external stakeholders, and deliver quality outcomes within agreed scope and timelines. Strong project management skills, with the ability to independently manage data science projects, effectively plan and manage timelines, juggle competing priorities and deliver quality outcomes under pressure and tight deadlines, while engaging internal and external stakeholders. Strong analytical, critical thinking and problem-solving skills, with the ability to evaluate complex problems and determine appropriate data science approaches. Strong quantitative and statistical modelling skills, with practical experience in applying appropriate methods and techniques to solve complex data science problems. Experience working with structured and unstructured datasets, including data preparation and preprocessing for model development. Proficiency in Python for statistical modelling and machine learning, and SQL for data extraction, manipulation and querying. Experience with cloud-based data science and machine learning platforms, particularly AWS services, would be advantageous. Experience with MLOps practices, including model deployment, monitoring and lifecycle management, would be advantageous. Good command of written and spoken English.