Analyst Data Office
@ Public Service Division (Singapore)

Singapore, Singapore
SGD 60,000
On Site
Full Time
Posted 5 days ago

Your Application Journey

Personalized Resume
Apply
Email Hiring Manager
Interview

Email Hiring Manager

XXXXXXXX XXXXXXXXX XXXXXXXXX***** @psd.gov.sg
Recommended after applying

Job Details

About the Role

The Analyst Data Office will support PSD’s Data Engineering & Infrastructure team in collecting, verifying, and publishing public service workforce data.

Key Responsibilities

  • Communicate with agencies to clarify data collection requirements.
  • Analyse data to identify and correct logic errors.
  • Support the preparation of publications using collected data.
  • Write and edit Python scripts to automate data processing.

Competencies Required

  • Proficient in Microsoft Office (Excel, Word, Outlook).
  • Proficient in Python focused on data processing.
  • Experience in AI (e.g., LLMs, RAG) or Tableau is optional.
  • Detail-oriented, analytical, and comfortable working with numbers and data.
  • Effective communication skills and sound judgment.

Key skills/competency

Data Collection, Data Verification, Python, Automation, Analysis, Microsoft Office, Data Processing, Communication, Attention to Detail, Publication Preparation

Apply without a personalized resume

How to Get Hired at Public Service Division (Singapore)

🎯 Tips for Getting Hired

  • Research PSD's culture: Understand their mission, values, and recent news.
  • Customize your resume: Highlight relevant data and Python skills.
  • Emphasize your analytic skills: Detail your data processing experience.
  • Prepare for technical questions: Review Python scripts and data verification techniques.

📝 Interview Preparation Advice

Technical Preparation

Review Python scripting basics.
Practice data error analysis exercises.
Familiarize with Microsoft Excel functions.
Understand automation script techniques.

Behavioral Questions

Describe a past data error resolution.
Explain how you manage communication challenges.
Discuss teamwork in data projects.
Describe handling tight deadlines.

Frequently Asked Questions