How Singapore Startups Are Using Data Science: 5 Real-World Examples

Data science is accelerating the innovation process in the startup sector in Singapore, encompassing diverse areas such as healthcare, waste reduction, safety compliance, and gig economy platforms, among others. Data science is driving significant impact, competitive edge, and scalability in this context. These reasons have made the discipline an ideal option for students and professionals, as it enables them to make a significant impact across various industries and earn a good income as well. For example, the average monthly base pay of a data scientist in Singapore is SGD 7,000, and it can increase to SGD 9,000.

This blog will discuss the five most prominent examples of real-world data science projects by startups based in Singapore. It will also discuss the common takeaways and success factors for data science startups in Singapore.

Source: Glassdoor 

Real-World Data Science Projects by Singapore Startups

The data ecosystem in Singapore has grown substantially over the last few years, and there are good reasons for this growth as well. The country has consistently led the way in adopting new technologies worldwide, and data science has been no exception.

Speedoc

Speedoc is a virtual healthcare solutions platform and clinic based in Singapore that carries out various data science projects. It uses data science to enhance patient care and improve its services. It also utilises data analytics to optimise operations, drive innovation in healthcare delivery, and personalise patient experiences. The following are the most prominent examples of how Speedoc is using data science in Singapore:

Core Areas	Specific Factors
Remote Patient Monitoring and Command Centre	Data-Driven Insights Artificial Intelligence (AI)-Powered Triage Virtual Ward Management
Logistics and Resource Optimisation	Route Optimisation Roster Management Workflow Automation
Improving Patient Experiences	Personalised Care Better Access to Care Economical Care
Innovative Healthcare Solutions	H-Pulse Pro Device Home-Based Care

Also Read: How to Start a Career in Data Science in Singapore

Lumitics

Lumitics is a food-waste technology company based in Singapore and uses data science to assist commercial kitchens in reducing food waste. It employs AI-driven tools such as its tracker, named Insight, to capture food waste-related data, provide curated recommendations for minimising waste, and identify waste. These, in turn, improve sustainability and help save costs. The following are the most prominent ways in which Lumitics uses data science:

• Data Collection.
• Data Analysis.
• Recommendations and Insights.
• Impact Measurement.

k-ID

k-ID is a company that specialises in identity verification and digital security. Based in Singapore, it uses data science startup ideas to provide privacy-preserving and age-estimation solutions for online platforms. For example, its Facial Age Estimation (FAE) solution operates on devices, which would imply that it does not require the organisation or its associated service providers to collect and store biometric data. The most prominent ways in which k-ID uses data science are:

• Age Assurance Compliance.
• Approach to Preserve Privacy.
• Data Science Expertise.
• Global Standards.
• Expanding Services.

Also Read: Best Programming Languages for Data Science in 2026: Python, R, or SQL?

Quest Global

In Singapore, Quest Global utilises data science to create solutions in areas such as healthcare, digital engineering, and thoughtful city planning. Other regions include:

• Healthcare.
• Smart City Planning.
• Digital Engineering.
• Standard Data Science Applications.

Also Read: Is a Master’s in Data Science worth it for a Career Switch in Singapore?

Virtual Singapore

When it comes to real-world data science projects, Virtual Singapore is a leading name that utilises data science to develop a 3D digital twin of Singapore, incorporating real-time data from various sources to support urban planning, resource management, and disaster prevention. Virtual Singapore employs data science in the following domains:

• Dynamic 3D Modelling.
• Data Integration.
• Predictive Analytics.
• Scenario Testing.
• Disaster Prevention.
• Resource Management.
• Community Engagement.

Also Read: How to Find a Data Science Mentor in Singapore – Tips for Networking and Guidance

Common Success Factors & Takeaways for Data Science Startups

In terms of startup ideas for data science in Singapore, such entities can achieve success by focusing on strong data foundations, building skilled workforces, and leveraging government support.

Common Success Factors	Key Takeaways
Powerful Data Foundation Strategic Support from Government Skilled Workforce Problem-Solving Focus Collaboration Adaptability to Upcoming Technology Customer-Focused Approach	Leveraging Government Programs Investing in Data Infrastructure Prioritising Data Literacy Building Strong Networks Focusing on Delivering Value through Business Developing Data-Powered Cultures

Also Read: Building a Data Science Portfolio: Key Elements for Success

Build Skills with upGrad’s Data Science Programs 

The online data science and analytics courses available through upGrad can provide students with the skills and knowledge necessary to work on data science business projects and contribute meaningfully to them as well. These programs help them elevate their data skills and master the discipline of data science, thanks to leading courses from some of the world’s top online universities.

• Master of Science in Data Science, Liverpool John Moores University
• Executive Diploma in Data Science and AI, IIIT Bangalore
• Post Graduate Certificate in Data Science & AI (Executive), IIIT Bangalore

FAQs on How Singapore Startups Are Using Data Science

Q: How can startups gather and clean production-level data?
Ans: To clean and gather production-level data, startups can implement robust data collection strategies, utilise the appropriate data cleaning tools, and establish transparent policies for data governance.

Q: How do startups validate ML models in live settings?
Ans: Startups validate machine learning (ML) models in live settings by regularly monitoring performance, using shadow deployments to compare model behaviour with current systems, and conducting A/B tests.

Q: What tools power sensor/image-based ML solutions?
Ans: Image- or sensor-based ML solutions rely on a combination of hardware and software frameworks and libraries that facilitate data collection, model training, preprocessing, and interference mitigation.

Q:  Which data science roles are in demand in startup ecosystems?
Ans: The data science roles in high demand in startups are:

• Data Scientists
• Data Engineers
• Machine Learning Engineers
• Data Analysts
• Business Intelligence Developers

Q:  What support does upGrad provide to build startup-ready portfolios?
Ans: upGrad offers the following support mechanisms and resources to help its students create startup-ready portfolios:

• Personalised Mentorship
• Community Access
• Career Guidance
• Job Placement Assistance
• Continued Resource Access