- Perform deep-dive statistical analysis and build machine learning models for tasks like classification, regression, clustering, and recommendation.
- Design, develop, and maintain scalable and efficient data pipelines to automate data collection, cleaning, and feature generation.
- Collaborate with business stakeholders or cross functional team to define problems, identify key metrics, and present data-driven recommendations.
- Develop and implement A/B testing frameworks to validate hypotheses and measure model impact.
- Work with AI Engineers to prepare models for production, providing clean code and well-engineered features.
- Implement governance, quality checks, and compliance standards (PDPA).
- Provide insights to business units through advanced analytics and visualization.
Requirements:
- Advanced degree (Bachelor or Master's degree) in Statistics, Mathematics, Computer Science, Economics, or another highly quantitative field.
- Minimum of 5+ years of experience in a data science role, with a proven portfolio of projects demonstrating impact.
- Significant experience (3+ years) in data engineering tasks (e.g., building ETL/ELT pipelines, data warehousing).
- Excellent analytical and problem-solving abilities.
- Strong communication and interpersonal skills.
- Ability to manage multiple projects and deliverables simultaneously.
Location(s):
Seatrium (SG) Pte. Ltd.
Pioneer Yard
50 Gul Road, Singapore
(Islandwide transport provided)
Working Hours:
Monday - Thursday: 8:00am - 5.15pm
Friday: 8:00am - 4.30pm
Interested candidates are invited to send us an updated resume with your current and expected salary and earliest availability.
We regret that only shortlisted candidates will be notified.
Please note that your personal data disclosed to Seatrium Limited and our group of companies, shall be used for the purposes of evaluation, and processing in accordance with our recruitment processes and policies. By providing your personal data, you have consented to the aforesaid purpose under the provisions of the Personal Data Protection Act 2012.