Data Management Consultant

CAMRIS is an international development and research firm that realizes innovative solutions to development challenges through high-quality, cost-effective program and research management services. With experience working in more than 100 countries, we combine our proven systems with today’s most effective, evidence-based best practices to improve development outcomes. We apply a customized, customer-centric business approach to offer greater value to our clients.

We implement USAID/Nepal’s Monitoring, Evaluation and Learning (MEL) Activity, a five-year project that provides performance management support to USAID/Nepal and its implementing partners (IPs) in the form of planning and monitoring, evaluations and studies, and knowledge management. The project is responsible for providing M&E capacity building support to both USAID/Nepal and IP staff.

The MEL team seeks one or two data management consultants to prepare three related datasets for upload in the US Government’s Digital Data Library. These datasets were undertaken as part of an impact evaluation baseline survey of the USAID/Nepal-funded Hariyo Ban-II project. The largest dataset of household data contains 6,900 variables. A dataset of group interviews from Community Forest User Groups contains 2,500 variables. A dataset of forest plots contains 1,000 variables. The datasets are mostly in Excel format currently, with a few as databases, and analysis scripts written in R. Further, some information will need to be gathered from Survey2Go to see how the program’s looping affected variables. Uploads need to be made in two or three languages including CSV and STATA and/or SPSS, or R or SAS.

In order to make those datasets ready to upload in the format DDL requires, the task at hand will include the below major actions, as well as others as required.

The consultant(s) will be responsible, in coordination with others, for:
* Reviewing materials for familiarization.
* Identifying personally identifying information in the dataset (per DDL rules) and suggest what to remove.
* Preparing csv data files.
* Labeling check of variables/missing data.
* Shifting columns at appropriate place.
* Converting to binary dummy coding.
* Deleting redundant (looping) and adjusting coding of multiple response questions.
* Deleting blank variables.
* Preparing communications for CAMRIS to communicate with USAID to determine and remove personally-identifiable information.
* Computing values after adjusting coding to compare and confirm with previously reported figures by running the syntax.
* Revising the questionnaire to align with changes.
* Breaking datafiles into as many datafiles as required to contain fewer than 500 variables.
* Deleting personally identifiable information.
* Preparing value labels.
* Preparing codebook.
* Compiling indicator definition and details.
* Preparing DDL information sheet.
* Compiling informed consent document.
* Preparing readme file.
* Compiling syntax.
* Coordinating with and responding to MEL team members.
* Uploading data file and required supplementary information.
* Performing any other tasks as required to upload the datasets.
* Six or more years’ experience managing and manipulating large quantitative datasets required, 8-10 years preferred.
* Experience manipulating data with Excel (flat) files required.
* Experience with STATA statistical software required.
* Experience with SPSS and R strongly preferred.
* Experience with filed-based data collection/survey software required.
* Experience with SurveytoGO Platform or another survey platform (such as Survey Solution) with looping logic strongly preferred.
* Knowledge of USAID’s Data Development Library (DDL) preferred.
* Thematic understanding on forest plot, community preferred.

CAMRIS International offers competitive salaries and comprehensive benefits. Please submit your resume online at www.camris.com. CAMRIS is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, protected veteran status, disability status, or any other characteristic protected by any applicable federal, state, or local law.