Leads research conceptualization, methodological and sampling design, instrument development, ethics governance, fieldwork, data quality assurance, quantitative and qualitative analysis, and report writing. Responsibilities include using R, Python, Stata, or similar tools; applying machine-learning techniques; analyzing microdata; visualizing findings; and translating evidence into policy-oriented communications. The role also requires stakeholder engagement, presentations, project management, and prior experience in the skill ecosystem.
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.
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
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Delhi, India's capital city, is a place where tradition and progress co-exist. While Old Delhi is known for its rich history and bustling markets, New Delhi is defined by its modern architecture. It's clear the region places a strong emphasis on preserving its cultural heritage while embracing technological advancements, particularly in artificial intelligence, which plays a central role in shaping the city's tech landscape, fueled by investments in research and development.
