Description

The Associate Program Data Management is a member of the PDM sub-team and the larger Monitoring, Learning and Information Systems (MLIS) team, and is responsible for quality data management for both paper and electronic data across all the countries of operations. This is an exciting position that will involve managing data officer(s) and data entry clerks, cleaning and submitting data to program teams or analysis team for analysis. The person must be systematic and loves working with big data and attentive to details. S/he will work closely with other MLIS team to ensure data is entered as error free as possible while adhering to set timeline

Duties and responsibilities

Data Cleaning

• Support data cleaning needs across all Evidence Action programs within the countries of operation
• Generate, clean and provide ad hoc datasets as may be required by different programs
• Able to multi task data cleaning assignments for different programs.
• Document all data cleaning processes and steps in the courses of his or her work
• Develop programs/code that check completeness and clean all program data
• Give timely feedback to the data collection team on quality of data and area of improvement/training
• Keep track of any recurring data quality issues arising from the exercising with a viewing of informing when re-training is needed.
• Support the development of comprehensive training manuals to suit the needs of different programs
• Any other Data Management task as assigned by their supervisor
• Coordinate the training of data entry staff.
• Supervise the activities of the data entry casuals by coordinating with the data officer.

Team Management

Data Analysis

• Conduct quarterly analysis on the quality of work of data collection officers and provide feedback to data collection lead highlighting area of further re-training.
• Develop comprehensive data cleaning guide for all data collected within the organization
• Verification and analysis of accuracy of geodata collected for all installed dispenser
• Any other analysis to improve quality, completeness and accuracy of data that might be assigned by the Supervisor.

Key performance Indicators

• Delivery of clean and complete program quarterly monitoring data within the required time to the data analysis team.
• Delivery of clean and complete process monitoring and coverage validation data within the required time to the data analysis team.
• Timely delivery of complete/programmed surveys for use by the Field Monitoring Team.
• Maintain documentation of data cleaning done in the course of data cleaning (either in syntax or notes)

Requirements

• Minimum Bachelor’s degree in economics, statistics, or any other relevant field
• Minimum of 3 years of experience in quantitative research methods and data management, preferably with large and/or complex datasets.
• At least 1-year full time experience conducting data cleaning using Stata, R, Python or MatLab for large datasets (mandatory)
• Experience in working with and programming data entry interfaces using a variety of applications both purchased and open source. Knowledge of SurveyCTO, CSPro, ODK, KoBo and Access will be an added advantage (SurveyCTO and/or ODK highly preferred)
• Well conversant with the use of MS Office application especially Excel.
• Ability to work under pressure in a working environment that changes suddenly to accommodate new data needs
• Strong interpersonal and communications skills to work effectively with a team that is geographically dispersed.
• Self-directed/self-motivating personality, with proven ability to manage demands from multiple supervisors while adhering to program deadlines and priorities.
• Strong critical and analytical thinking skills.
• High attention to details and well organized.
• Should have real passion of working with data, be able to think and tell a story from the data (Key desirable)
• Intellectual flexibility and willingness to form and adjust opinions based on evidence
• Quick to learn, motivated to self-teach and capable of independently translating new knowledge into practice.