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:

- 3-8 years of working experience in the relevant field depending on the requirement of the level.
- Proficient in Microdata analysis using any software package like R, Python, Stata
- Prior experience of working in skill ecosystem
- Deployed ML techniques in analysis
- Proficient in PowerBI, Excel & Powerpoint
- Ability to write analytical reports

📌 Sr. Analyst (Research) (Delhi)
🏢 NSDC India
📍 Delhi