Data Scientist

About the role

The Data Scientist collaborates with multidisciplinary teams, business stakeholders and external partners to apply advanced analytics, statistical methods and machine learning to complex business challenges. The role translates business needs into analytical solutions, develops and evaluates models, and works with technical teams to operationalise them in existing analytics platforms and production environments, supporting STB's strategic objectives and the growth of Singapore's tourism industry.

Key responsibilities

• Translate complex business problems into data science objectives and choose appropriate statistical and machine learning approaches.
• Design, develop and implement statistical and machine learning models, with robust validation and evaluation of performance, accuracy and reliability.
• Perform data exploration, preprocessing and feature engineering on complex datasets.
• Work with data engineers to deploy models into production, monitor performance over time and recommend recalibration or retraining.
• Maintain documentation on model methodology, assumptions, validation results and operational requirements.
• Own data science projects, managing scope, quality, timelines, risks and stakeholder expectations.
• Collaborate with data engineers, DevOps engineers, product managers, business analysts and stakeholders.
• Review and improve data collection methods, evaluate new data sources and source complex datasets internally and externally.

Required profile

• At least 3-5 years of experience developing, evaluating and implementing statistical and machine learning models for real-world business problems.
• Ability to independently manage data science projects and deliver quality outcomes within agreed scope and timelines.
• Strong project management, analytical, critical thinking and problem-solving skills.
• Good command of written and spoken English.

Required skills

• Quantitative and statistical modelling; experience with structured and unstructured datasets and data preprocessing.
• Python for statistical modelling and machine learning, and SQL for data extraction, manipulation and querying.
• Advantageous: cloud-based platforms such as AWS, MLOps practices, and model deployment and monitoring.