Data Analyst
1. Educational Background:
- Minimum Requirement: Bachelor’s degree in Data Science, Statistics, Computer Science, Information Management, or a related field.
- Preferred Qualification: Master’s degree in Data Science, Business Analytics, or a related discipline.
- Certifications: Certifications in data analysis software (such as SAS, R, Python), big data technologies, or business intelligence platforms like Tableau or Power BI are highly desirable.
2. Professional Experience:
- Minimum Years of Experience: At least 3-5 years of experience in data analysis, preferably within the sectors relevant to the project such as government, healthcare, or public administration.
- Specific Experience: Experience with managing and analyzing large datasets, creating reports, and providing actionable insights. Experience in working with compliance or regulatory data within a structured project environment is a plus.
3. Skills and Competencies:
- Analytical Skills: Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
- Technical Skills: Proficiency in database management and data analysis software (e.g., SQL, Python, R), as well as advanced proficiency in spreadsheet tools like Microsoft Excel.
- Communication Skills: Excellent communication skills to effectively present findings and insights to both technical and non-technical stakeholders.
4. Knowledge:
- Statistical and Mathematical Knowledge: Solid understanding of statistical testing and modeling techniques that are essential for analyzing complex data.
- Data Privacy and Security Knowledge: Awareness of data privacy standards and best practices, especially in handling sensitive or personal data, which is crucial in government and healthcare sectors.
5. Personal Traits:
- Problem-solving Abilities: Ability to approach complex data-related problems with a clear analytical methodology and solve them efficiently.
- Attention to Detail: Meticulous attention to detail to ensure the accuracy of data and analysis.
- Proactivity: Proactive in identifying trends, anomalies, and opportunities in the data to drive project objectives.
6. Responsibilities:
- Data Collection and Management: Collect, process, and clean data from multiple sources to ensure it is ready for analysis.
- Data Analysis and Reporting: Analyze data using statistical techniques and tools, and create reports and dashboards that clearly communicate the findings and recommendations.
- Support Decision Making: Provide data-driven insights to support strategic decisions and project initiatives.
- Continuous Learning: Stay updated with the latest trends, tools, and technologies in data analysis to enhance project outcomes and efficiency.