Research Data Systems Consultant
Role details
Job location
Tech stack
Job description
We are looking for a consultant that will be responsible for leading and coordinating the end-to-end implementation of the Software within OCH, from early data architecture decisions, to complete data importation, to full internal deployment across countries.
This role is not limited to technical data handling. It sits at the intersection of:
- Quantitative research
- Data engineering & structuring
- Change management and internal enablement (desirable)
A strong understanding of quantitative research data and survey-based workflows is therefore essential, as decisions made during data preparation and labeling directly affect how data can be explored, compared, and reused for future research objectives., Roll-out planning & coordination:
- Lead the operational roll-out of the Software in line with OCH's phased implementation plan (preparation, full data importation, internal deployment)
- Coordinate closely with OCH research leadership and country teams to ensure alignment on objectives, timelines, and priorities
- Act as the main point of contact for Software-related operational questions within the organization
- Liaise with the Software's technical team in case of questions / required support during implementation phase
Data architecture, labeling & importation
- Define and implement a coherent data architecture and variable labeling strategy adapted to cross-project & cross-country comparability, as well as long-term reuse of polling data
- Lead pilot imports of selected datasets to test and refine structure and labeling conventions
- Manage the large-scale importation of existing datasets (~50 datasets) into the Software, ensuring quality, consistency, and documentation
Internal enablement & adoption
- Design internal onboarding and training content for future Software users of country teams and global research team
- Plan and coordinate onboarding sessions (country-level and/or global) - including scheduling, preparation materials, and follow-up
- Provide time-bound technical support to encourage early adoption and structured uptake
Requirements
Do you have experience in Stata?, This role requires a hybrid profile, combining research-related knowledge & experience with operational, technical and data fluency.
Essential
- Some experience working with quantitative polling or opinion research data
- Understanding of survey design and polling data structures (questionnaire logic, codebooks, weighing, variable harmonization, analytical use cases, etc.)
- Fluency with survey data formats and tools (e.g. SPSS .sav, R, Stata, or similar), including managing large numbers of variables and complex metadata
- Ability to make informed decisions about data labeling and structuring based on research objectives, not just technical constraints
- Strong organizational skills and ability to manage complex, multi-country data workflows
Highly desirable
- Prior experience with data platforms for survey/polling data analysis
- R coding proficiency (Software supports R for easier cross-project labeling harmonization)
- Experience supporting or training non-technical users in data tools
- Familiarity with international or multi-country research environments, * Analytical and methodologically rigorous
- Comfortable translating between technical data work and research questions
- Pedagogical mindset and strong communication skills
- Pragmatic, structured, and detail-oriented
- Able to work autonomously while collaborating closely with diverse teams
Expected outcomes
- A fully populated and well-structured platform containing all existing quantitative datasets (essential)
- A methodological & guideline document on data labeling / structuring for future imports and adapted to cross-project and cross-country analysis (essential)
- Onboarding and training content (format TBD): a set of tools/guideline documents that OCH teams can refer to as desired for technical support (desirable)
- Internal teams equipped to explore and use the Software more independently, including non data-specialists (desirable)