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Salary Guide: How Much Can A Data Scientist Earn In Singapore?

The median salary for a junior data scientist is $7,500.

The exponential growth of digital services has led to the development of the data science industry, which is responsible for the analysis, modelling, and visualisation of data. In fact, it is also highlighted as a primary growth area in the SkillsFuture 2023 Report.

Moreover, it’s a highly sought-after skill by businesses aiming to refine their operations and make informed decisions. The lack of data scientists, which is used as an umbrella term to encompass various data science-related roles in the market, has led to lucrative salary offers for these professionals. If you’re considering a career as a data scientist, here’s how much you can earn in Singapore.

Who Is A Data Scientist

A data scientist is an analytical professional who’s responsible for designing and implementing processes and logic to extract, transform, and load data from multiple data sources to obtain business insights and recommendations. It requires a combination of conventional and technological skillsets such as mathematics, advanced statistical analysis, machine learning techniques, and predictive modelling.

Depending on the industry they are in, a data scientist may also be referred to as a data analyst, data architect, data engineer, or statistician.

How Much Can A Data Scientist Earn In Singapore

Based on tech-talent platform NodeFlair’s 2024 report, salaries for data scientists increased by 11.3% year-on-year.

Based on the same report’s findings, the median monthly salary for a junior data scientist is $7,500. On the other hand, a Lead data scientist, who might be managing a small team of data scientists, could earn as much as $17,000 per month.

Role (Level of Experience) Lower-End Salary Median Salary Upper-End Salary
Junior (0–2 years) $5,500 $7,500 $9,750
Mid (2–5 years) $7,000 $9,000 $12,000
Senior (5+ years) $7,750 $10,000 $13,000
Lead $13,125 $14,187 $17,000


The higher salaries for data scientists are due to the demand for their advanced analytical skills and the complexity of their work.

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What Role Does A Data Scientist Perform

As a data scientist, you would be required to perform some of the following job functions based on your industry and experience:

  • Analyse and prioritise the business requirements to be aligned with the organisation’s directions.
  • Apply data analysis and data profiling to improve the clarity, quality and integrity of valid data to achieve the business requirements and needs of the organisation.
  • Design and drive the solution based on the business problem and hypotheses.
  • Design an extraction process for consolidating data from multiple data source systems.
  • Design the process to transform extracted data into structures and data types that align with the business rules incorporated in the target system.
  • Develop the data visualisation that frames the insights or stories for the audience.
  • Evaluate and select the appropriate big data technologies and tools.
  • Explore the data in the analytics platform or organisation to become familiar with the data available for analysis.
  • Identify and review key information sources related to the business problem or needs.
  • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes.
  • Perform analysis on the data to prove the hypothesis and obtain business insights using the relevant programming language or tools for big data analytics.
  • Select the runtime environment for the statistical model to be deployed and the user requirements with the relevant stakeholders.
  • Validate and resolve any conflict related to the information and business requirements with the stakeholders.

Education Qualification Required To Become A Data Scientist

Being a data scientist requires a wide range of complex planning, analysis, and modelling. To ensure candidates are able to perform this role, most companies would require at least a bachelor’s degree in the following fields: business analytics, computer engineering, computer science, data analytics, engineering, information technology, information systems, mathematics, or statistics.

In some instances, you may be required to possess post-graduate qualifications in data science or computer science if you have bachelors in unrelated fields.

Another way to become a data scientist is to have relevant professional certification or boot camp experience. This could mean taking up the required technical skillsets or gaining project experience in order to perform the job functions as a data scientist.

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Skills Required For A Data Scientist

As a data scientist, you will need to evaluate large amounts of unorganised and freeform data. This requires various technical and non-technical skillsets. Here’s a detailed look at the key skills that you’ll need:

Technical Skills Required

Programming Languages: In order to sort through, analyse, and manage large amounts of data, you would be required to know programming languages such as the following:

  • Python or R: While both are free, open-source languages, Python is a general-purpose programming language, while R is a statistical programming language. These are required to sort through, analyse, and manage large chunks of data.
  • SQL: Not only should you understand how to apply algorithms, but you also need to retrieve data for analysis. One tool that can aid you with this is Structured Query Language (SQL). It is specifically designed to access data from the database where it is stored and communicate with it.
  • SAS: Having a good grasp of analytical tools like SAS is required to extract valuable insights from data sets. You may also be required to use multiple analytical tools other than SAS, like QlikView, Splunk, and Apache Hadoop.

Data Visualisation: Tools like Tableau, PowerBI, and Excel help data scientists translate information and data into visuals like 3D plots, relationship maps, histograms, bar charts, and line plots. These data visualisation tools help data scientists spot trends, patterns, and outliers in a set data.

Machine Learning: Having an in-depth knowledge of techniques such as supervised machine learning, unsupervised machine learning, reinforcement learning, decision trees, and neural networks could be useful for a data scientist.

Handling Unstructured Data: As part of managing big data, data scientists also need to handle unstructured data (text, images, and audio) using techniques like natural language processing (NLP) and computer vision.

Non-Technical Skills Required

In addition to the technical skills, you’ll also need the following non-technical skills:

  • Communication skills: As the role of a data scientist requires you to translate complex data findings into clear, concise, and actionable insights for technical and non-technical stakeholders, you must have strong communication skills to get your points across to the relevant parties.
  • Business Acumen: In order to create valuable insights into the problems that are crucial to the business, you need to understand its processes, goals, and strategies so that you can align data projects that are aligned with the organsiation’s objectives.
  • Teamwork: Another soft skill that is important for a data scientist is teamwork, as no data scientist works in isolation. You have to work with people from different departments in the organisation to deliver the appropriate solution to the business problem.

Employment Opportunities For Data Scientists  

Based on NodeFlair’s 2024 report, the top 15 searched Singapore-based companies for tech roles are:

Among these companies, the FAANGs—Meta, Apple, Google—generally around 30% to 50% more than the median.

Beyond the well-established companies in the IT and banking sectors, there are many startups in the data science field, like Crayon Data and Tookitaki, where there might be job opportunities for data scientists.

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Future Of Data Scientists

Demand for data scientists is expected to grow in the coming years, given the country’s push towards being a Smart Nation as it positions itself as a technological hub, attracting both tech startups and big tech companies.

While being a data scientist requires continuous learning, curiosity, and skill development, the ever-changing technological landscape could potentially present exciting opportunities and challenges that could make for a rewarding career if you’re able to overcome its complexities.

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