Company description:
SingHealth Polyclinics, a leader in family medicine, provides seamless, patient-centred and preventive healthcare that is affordable and accessible to all.
SingHealth Polyclinics' network of polyclinics provides primary healthcare services to the community. It plays an integral role in promoting a healthy lifestyle within the community and empowering families with the knowledge of common health issues, care and treatment options.
SingHealth Polyclinics is a member of Singapore Health Services (SingHealth) - a public healthcare cluster which also manages three hospitals and five specialty centres.
Job description:
Job Description
You will support evidence-based decision-making across our healthcare programmes by translating data into insights that inform clinical and operational decisions. You will also help maintain the data foundations needed to ensure that these insights are accurate, consistent and reliable.
This is an end-to-end role covering requirements gathering, data extraction and preparation, analysis, dashboard development and the communication of findings to stakeholders. The role also includes essential data management work, such as maintaining data definitions and documentation, tracing reported figures to their source and ensuring that data assets remain accurate and fit for use.
As you gain experience, you will take ownership of analytical projects supporting specific healthcare programmes and national planning priorities. Over time, you will develop subject matter expertise and independently scope, deliver and present analyses that support decision-making.
Job Responsibilities
- Deliver end-to-end data analytics and machine learning projects, including problem definition, data preparation, analysis, validation and presentation of findings.
- Provide analytical support for national healthcare initiatives and planning priorities.
- Maintain key data management assets and processes, including the electronic medical record data dictionary, data definitions, data lineage documentation, data quality checks, source-to-report traceability, data change requests and user acceptance testing.
- Develop and maintain dashboards that support clinical and operational decision-making.
- Perform other duties as assigned.
Job Requirements
- Bachelor's degree in Business Analytics, Computer Science, Information Systems, Statistics, Mathematics, Engineering, Economics, or a related quantitative, computing or analytics discipline.
- Preferably 0 to 3 years of relevant experience in data analytics, data science, business intelligence, data management or healthcare analytics. Fresh graduates with relevant internships, academic projects or portfolio work are welcome to apply.
- Working knowledge of SQL and Python for data extraction, transformation, validation and automation, with a willingness to further develop these skills on the job.
- Experience with Tableau or a similar data visualisation tool, with an understanding of dashboard design, usability and data storytelling.
- Familiarity with exploratory data analysis, statistical analysis, trend analysis and segmentation techniques.
- Familiarity with data dictionaries, metadata management, data quality checks, business rules and change request processes.
- Awareness of responsible AI practices, including the appropriate use of generative AI and large language models, their limitations and risks, validation requirements, and the importance of human oversight.
- Strong communication skills and attention to detail, with the ability to work with incomplete or imperfect data, explain limitations to technical and non-technical audiences, document work clearly, and work both independently and collaboratively.
Candidates with limited or no full-time work experience are welcome to apply if they have relevant internships, academic projects or portfolio work. Candidates with stronger experience and the ability to independently manage workstreams may be considered for the Senior Analyst position.
Advantageous
- Experience in healthcare, public health or clinical operations, or familiarity with electronic medical record systems.
- Exposure to machine learning or advanced analytical methods, such as supervised and unsupervised learning, forecasting and causal inference, and to libraries such as pandas, scikit-learn or statsmodels.
- Experience with modern data and machine learning platforms, such as Databricks, MLflow or cloud-based environments.
- Experience in the public sector.