Sr. RMLE Manager

Role & responsibilities

The Research Manager will undertake activities represented by but not limited to below, under the direction of the Chair-Steering Committee/CTM, RMLE:

• Developing detailed code dictionary and indicator matrix for relevant studies and finalizing their analytical plan to meet the requirements of the internal and external stakeholders and for providing future directions to the program.
• Formulating scientifically valid and operationally feasible analytical process for the studies to be undertaken by RMLE team, optimized with available time to ensure efficiency.
• Leading the overall analysis of the data collected in various studies and planning the advanced analysis him/herself.
• Identification of crucial and programmatically relevant findings from the analyzed results.
• Conceptualizing relevant data visualization methods (charts/graphs/tables) and materials based on RMLE data and finalizing them to meet the requirements of the internal and external stakeholders and for providing future directions to the program.
• Ensuring that for all collaborative generation of analytical findings based multi-partner studies or those with multiple stakeholders, all the inputs on analytical plan, syntax and findings are addressed or provided back in an appropriate and timely manner.
• Developing, correcting, archiving and hard-coding the syntax written for data analysis (basic and advance) using statistical analytical software (SAS/R/STATA/Python/SPSS etc..)
• Ensure capacity building of the team members on analytics.
• Spearhead the capacity building of program team and Government counterparts in the area of data analysis and utilization
• Travel within or beyond program areas, as per the project need

Preferred candidate profile

Essential

• Masters in a relevant discipline with about 3 years experience in quantitative data analytics and experience of analyzing large scale data
• Proficiency in using statistical analytical software (SAS/R/STATA/Python/SPSS etc..)

Preferred

• Deep familiarity with advanced data analytics.

Experience with field data and national survey data

Data cleaning and management analytical skills:

Demonstrated ability to

• Develop data structures and check data configuration during tool testing, study piloting, validation & zero round
• Clean, collate and organize large datasets of multiple type and layers
• Manage complex, multidimensional, multisource datasets

Data analytical and interpretational skills:

Demonstrated ability to analyze and interpret complex, multilayered monitoring, evaluation & research data.

Proficiency in using statistical analysis software like (SAS/R/STATA/ Python/SPSS) for analyzing quantitative data.

Teamwork & Collaboration / People skills:

A team player, multitasker, ability to mentor team members