Data Scientist / Senior Data Scientist (Advanced Analytics) – AMK
COMBUILDER PTE LTD
Ang Mo Kio, Singapore
**
Python programming experience
Sql database querying skills
Machine learning model development
**
COMBUILDER PTE LTD is seeking a Data Scientist / Senior Data Scientist for advanced analytics projects in the public sector, focusing on machine learning and AI solutions. The ideal candidate should have at least two years of relevant experience, strong Python and SQL skills, and familiarity with data processing and modeling techniques.
**
Job Summary
This role involves developing advanced analytics and data science solutions using both structured and unstructured data for large-scale public-sector projects.
Candidates will manage the full analytics lifecycle from requirement gathering and data scoping to model deployment and monitoring.
Prior experience in government, public-sector, or defense-related projects is highly advantageous for this position.
Matching Summary
Match Score: 75
**
COMBUILDER PTE LTD is seeking a Data Scientist / Senior Data Scientist for advanced analytics projects in the public sector, focusing on machine learning and AI solutions. The ideal candidate should have at least two years of relevant experience, strong Python and SQL skills, and familiarity with data processing and modeling techniques.
**
Skills & Requirements
Must-have
Python programming experience
SQL database querying skills
Machine learning model development
Pandas NumPy Scikit-Learn usage
XGBoost and PySpark frameworks
Full analytics lifecycle management
Nice-to-have
AWS SageMaker platform knowledge
Generative AI and LLM exposure
RAG and document intelligence skills
Data engineering and ETL pipelines
Git CI/CD and test-driven development
Public sector or defense project background
Key Requirements
Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or Business Analytics
Minimum 2 years of experience in data analytics, data science, or machine learning
Eligibility for MSD Security Clearance CAT 1 strongly preferred