Leads research conceptualization, methodological and sampling design, instrument development, ethics governance, fieldwork, data quality, quantitative and qualitative analysis, and report writing. The role requires managing research projects, applying machine learning and statistical methods, using analytical software, communicating findings, and translating evidence for policymakers and other stakeholders. Prior experience in the skill ecosystem and strong analytical, reporting, presentation, and data-management capabilities are required.
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
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What you need to know about the Delhi Tech Scene
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.
