MEAL-Part timer

MARCH is looking for a MEAL -part timer for implementing conflict resolution projects and activities in Tripoli and Beirut.

Responsibilities:

• Follow-up with center coordinators for submitting data needed on time
• Entering data from source documents within time limits accurately and efficiently
• Review data for deficiencies or errors, correct any incompatibilities, translate from Arabic to English and vice versa if possible and check output
• Verify data accuracy by comparing it to source documents.
• Communicate effectively with team members to clarify any data discrepancies
• Research and obtain further information for incomplete documents
• Apply data program techniques and procedures
• Generate reports, store completed work in designated locations and perform backup operations
• Assist with any additional tasks as requested to meet the needs of the role.

Qualifications:

• Education: Candidate (preferably to be fresh graduates and university students) should have also a background on data sciences, data analytics
• Experience: Previous experience in data entry or related field preferred.
• Proficiency in using data entry software spreadsheets (e.g., Microsoft Excel, Google Sheets,), and data bases.
• Attention to detail: Exceptional attention to detail and accuracy in data entry.
• Analytical skills: Ability to review data for deficiencies or errors, correct any incompatibilities, and check the output.
• Teamwork and Collaboration skills with diverse teams members necessary for coordinating efforts, sharing insights, and collectively problem-solving.
• Flexibility and Adaptability to adjust their approaches and methodologies to meet evolving needs and circumstances.
• Problem-Solving skills when encountering various challenges during data collection, analysis, and reporting. Strong problem-solving skills are essential for overcoming obstacles and finding innovative solutions.
• Cultural Sensitivity knowledge while working in diverse cultural contexts, and understanding and respecting cultural differences are essential for conducting evaluations that are contextually relevant and respectful.