Support data-driven decisions by collecting, analyzing, and visualizing data. Collaborate remotely and meet project timelines in a part-time role.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Role Description The Data Insights Analyst (Remote, Entry-Level) role at Hulp bij dwingende controle is a part-time position focused on supporting data-driven decision-making. In this role, the analyst collects, organizes, and analyzes data from various sources to identify trends, patterns, and potential areas of improvement. The analyst prepares clear and concise reports, dashboards, and visualizations to support internal stakeholders and project teams. Daily tasks include validating data for accuracy, performing basic statistical analyses, and assisting with the development of data models. This is a remote role, requiring effective collaboration with colleagues through digital communication tools and adherence to project timelines.
Qualifications
- Strong analytical skills and the ability to interpret quantitative and qualitative data to generate actionable insights.
- Foundational experience or education in data analytics, including familiarity with data analysis tools or platforms.
- Basic knowledge of statistics, such as descriptive statistics, correlation, and hypothesis testing, to support data-driven conclusions.
- Clear and professional communication skills to present findings, collaborate with team members, and document methodologies.
- Understanding of data modeling concepts and the ability to support the design and maintenance of simple data models.
- Ability to work independently in a remote environment, manage time effectively, and meet deadlines in a part-time schedule.
- Proficiency with spreadsheets and/or entry-level data tools (e.g., Excel, Google Sheets; exposure to SQL or BI tools is an advantage).
- Relevant coursework or degree in a field such as data science, statistics, mathematics, economics, computer science, or related discipline is preferred but not required.
- Attention to detail, commitment to data quality, and willingness to learn new tools and techniques in data analysis.
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