Research Assistant (DBMI)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading academic institution in Singapore is seeking a Data Scientist to join their Division of Biomedical Informatics. The successful candidate will develop and implement AI and machine learning models to support clinical workflows. Key responsibilities include data preprocessing, statistical analysis, and creating visualizations to convey analytical findings. A bachelor's degree in a relevant field and proficiency in tools like SQL, Python, and visualization software are essential. Strong analytical and communication skills are crucial for success in this role.

Qualifications

• Bachelor's degree in a relevant field.
• Strong analytical and problem-solving skills.
• Proficiency in SQL, Python, and R for data manipulation.
• Experience with Tableau and Power BI for visualization.
• Excellent communication skills for presenting findings.

Responsibilities

• Clean and preprocess data to ensure accuracy.
• Apply statistical techniques to analyze large datasets.
• Develop and implement data models and visualizations.
• Communicate findings through reports and dashboards.
• Collaborate with teams to understand data needs.

Skills

Data analysis

Problem-solving

SQL

Python

R

Data visualization

Communication

Education

Bachelor's degree in Statistics, Mathematics, or Computer Science

Tools

Tableau

Power BI

Job description

Interested applicants are invited to apply directly at NUS Career Portal

Your application will be processed only if you apply via NUS Career Portal

We regret that only shortlisted candidates will be notified.

Job Description

Join the Division of Biomedical Informatics, Yong Loo Lin School of Medicine, NUS that advocates flexible and innovative working culture. Successful candidate will be part of a team of Data Scientists and Software Developers who partner clinicians to propose, develop, and implement advanced

Artificial Intelligence / machine learning models and pipelines to augment clinical workflows in NUHS cluster. The candidate will also ensure the adequacy and effectiveness of the models and tools for NUHS.

You will be responsible for the following:

• Clean and preprocess data to ensure accuracy, consistency, and completeness.
• Apply statistical techniques and data mining methods to explore and analyze large datasets.
• Identify meaningful patterns, trends, and correlations in the data.
• Develop and implement data models, algorithms, and visualizations to present findings effectively.
• Prepare and deliver clear and concise reports, dashboards, and presentations to communicate analytical findings to stakeholders.
• Collaborate with teams across the organization to understand their data needs and provide analytical support.
• Ensure data visualizations are intuitive, visually appealing, and easy to understand for non-technical audiences.
• Develop and implement data quality checks and validation processes to ensure data accuracy and integrity.
• Monitor data integrity over time and proactively identify and address any data issues or anomalies.

Qualifications

• Bachelor's degree in a relevant field (e.g., Statistics, Mathematics, Computer Science).
• Strong analytical and problem‑solving skills, with the ability to translate complex data into actionable insights.
• Proficiency in data analysis tools and programming languages (e.g., SQL, Python, R) for data manipulation, analysis, and visualization.
• Familiarity with statistical techniques and data mining methods.
• Experience with data visualization tools (e.g., Tableau, Power BI) to create interactive and visually appealing dashboards and reports.
• Knowledge of database concepts and experience working with large datasets. (Good to Have)
• Strong attention to detail and ability to handle and analyze data accurately.
• Excellent communication and presentation skills to effectively convey analytical findings to both technical and non‑technical stakeholders.
• Ability to work independently and collaboratively in a fast‑paced environment.