Sr. Analyst (Research)
Summary
The Senior Analyst will lead research conceptualization, data collection, and analysis within the skill development ecosystem. The role involves using tools like R, Python, and PowerBI to generate insights and policy-oriented reports.
A — Research Conceptualisation & Design
- Problem formulation: framing research questions; constructing testable hypotheses/propositions; literature synthesis and gap identification; developing conceptual/theoretical frameworks
- Understanding of skill ecosystem: Prior work or academic experience of skill ecosystem
- Methodological design: selecting research paradigm (quantitative / qualitative / mixed); choosing design (survey, experiment, case study, ethnography, longitudinal); operationalising constructs into measurable variables
- Sampling design: probability vs. non-probability strategies; sample-size and power estimation; weighting and representativeness; frame construction
- Instrument development: questionnaire and item writing; interview/FGD protocol design; scale development, piloting and validation
- Research governance: ethics and informed consent; securing IRB/ethical clearance and administrative approvals; data-protection and privacy compliance
B — Data Collection & Fieldwork
- Primary collection: survey administration (CAPI/CATI/paper); interviewing (rapport, probing, active listening); structured observation and field recording
- Field operations: enumerator recruitment, training and supervision; fieldwork logistics and scheduling; real-time monitoring
- Secondary data acquisition: sourcing administrative and government datasets; extraction, compilation and linkage; assessing provenance and fitness-for-use
- Data quality assurance: back-checks and spot validation; consistency and range checks; audit trails
C — Data Management & Analysis
- Data preparation: cleaning, de-duplication and outlier handling; missing-data treatment; coding, structuring and codebook/metadata documentation
- Quantitative analysis: descriptive statistics; inferential testing and regression; advanced methods (multivariate, psychometrics/IRT, equating, causal inference); tool fluency (R, Python, SPSS, Stata)
- Qualitative analysis: thematic and content analysis; grounded-theory coding; QDA software (NVivo, etc.)
- Interpretation & synthesis: pattern and trend identification; triangulation across sources; data visualisation for insight
D— Communication, Report writing
- Technical & academic writing: report structuring; publication and manuscript writing; citation and referencing discipline
- Dissemination: presentation and public speaking; data storytelling; conference/workshop facilitation
- Stakeholder engagement & policy translation: policy-brief writing; audience-tailored messaging; advisory and consultative engagement with policymakers, practitioners and the public
E- Transversal / Foundational skills
- Research ethics and integrity; project and time management; digital and data literacy; critical thinking and problem-solving; collaboration and teamwork; multilingual/field-language competence; adaptability under field conditions.
Requirements
1Academic Background:
· Masters or above in Economics, Statistics, Data Science
· Engineers with Data Science, Machine Learning, Data Analytics
· Any Other candidates from Social Science background can be considered on a case-to-case basis depending on the merit
1Experience:
1. 3-8 years of working experience in the relevant field depending on the requirement of the level.
2. Proficient in Microdata analysis using any software package like R, Python, Stata
3. Prior experience of working in skill ecosystem
4. Deployed ML techniques in analysis
5. Proficient in PowerBI, Excel & Powerpoint
6. Ability to write analytical reports