<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <br/><p><strong>Role Description</strong> This part-time remote role of Data Scientist / Data Visualization Analyst focuses on transforming community and program data into actionable insights. The person in this role will collect, clean, and analyze data related to member engagement, event performance, and networking outcomes, using statistical and analytical methods. They will design clear and compelling dashboards, reports, and visualizations that help stakeholders understand trends, measure impact, and make informed decisions. Daily tasks include collaborating with program leads to define data needs, building and maintaining analytics tools, interpreting findings for non-technical audiences, and recommending data-driven improvements to initiatives. The role also involves documenting methodologies, ensuring data quality, and supporting strategic planning with evidence-based insights.</p><p><strong>Qualifications</strong></p><ul><li>Strong foundation in Data Science and Statistics, with the ability to apply quantitative methods to real-world community and program data.</li><li>Proficiency in Data Analytics and Data Analysis for extracting insights, identifying trends, and evaluating the impact of initiatives.</li><li>Experience in Data Visualization, including building dashboards and visual reports that effectively communicate key metrics and narratives.</li><li>Hands-on experience with analytics and visualization tools (e.g., Python/R, SQL, Excel, Power BI, Tableau, or similar platforms).</li><li>Ability to translate complex findings into clear, actionable recommendations for non-technical stakeholders.</li><li>Strong problem-solving skills, attention to detail, and commitment to data integrity and ethical data use.</li><li>Effective written and verbal communication skills in English; familiarity with Arabic-speaking professional contexts is an advantage.</li><li>Prior experience working remotely and managing part-time workloads, with the capacity to collaborate across time zones.</li><li>Relevant academic background in Data Science, Statistics, Computer Science, Economics, or a related field, or equivalent practical experience.</li></ul> </div>