Data Scientist II

Innovations for Poverty Action (IPA) is a research and policy non-profit organization that discovers and promotes effective solutions to global poverty problems. Its Global Research and Data Science (GRDS) team provides technical support across its research portfolio and builds data science, data engineering, and research quality capabilities.

The Data Scientist II sits within IPA’s Global Research and Data Science (GRDS) team, which provides technical support across IPA’s research portfolio and builds the data science, data engineering, and research quality capabilities the organization relies on. The Data Scientist II owns technical projects from initiation to delivery, applying data science methods to concrete research and data support needs. The role combines hands-on technical work with the judgment to plan independently, anticipate risks, and recommend a course of action when problems require escalation. The Data Scientist II also serves as a technical reference for colleagues and represents IPA’s technical standards in work with country offices, sector programs, public policy teams, and external partners.

1. Lead assigned projects from initiation to delivery: plan tasks, track progress, anticipate risks, and report status and decisions to the manager without requiring follow-up.
2. Provide high-quality technical support to IPA country offices, sector programs, public policy teams, and research projects.
3. Develop Scopes of Work for direct technical support engagements and conduct research quality audits of ongoing projects.
4. Apply reliable technical judgment when advising internal and external partners.
5. Apply data science methods — including predictive modeling, natural language processing, large language models, and geospatial analysis — to concrete research and data support needs.
6. Develop and maintain SQL and Python data models, analytical pipelines, and other software solutions.
7. Deliver advanced technical outputs in SurveyCTO, Stata, R, SQL, Python, and other relevant tools.
8. Support testing and debugging of global data products and technical tools.
9. Ensure reproducibility through version control and well-documented workflows.
10. Prepare and maintain research datasets following reproducible data management practices.
11. Serve as a trusted technical reference for colleagues; guide Data Research Associates and other research staff on tools, workflows, and IPA’s research and data standards.
12. Provide code reviews and help maintain sound technical practices across the team.
13. Translate complex analytical findings into clear products for research and practitioner audiences, including data visualizations, briefs, and technical documentation.
14. Present analysis to internal and external stakeholders, adapting communication to audiences ranging from field teams to senior researchers and partners.
15. Stay current with developments in applied data science and machine learning relevant to development research, and share useful methods and tools with the GRDS team.

1. Bachelor’s degree or equivalent in computer science, data science, statistics, economics, social sciences, or a related quantitative discipline.
2. At least three years of professional experience in data science, data engineering, research support, or a related field, with a demonstrated record of owning and delivering technical projects independently.
3. Proven experience developing data science or data engineering projects in Python.
4. Proficiency in SQL for database querying and analytics.
5. Experience with version control systems such as Git, and with reproducible, well-documented workflows.
6. Strong quantitative skills and a solid understanding of core statistical concepts, including regression, hypothesis testing, and sampling.
7. Demonstrated ability to clean, manage, analyze, and document data.
8. Clear written and verbal communication in English, with the ability to document, explain, and present technical work.
9. Ability to build effective working relationships in a multicultural environment with sensitivity and respect for diversity.
10. At Data Scientist II level: proactive communication, surfacing progress, risks, and decisions without being asked, and experience mentoring or serving as a technical reference for peers.
11. Experience with cloud computing platforms (AWS, GCP, or Azure) and analytics databases (Snowflake, Databricks, Redshift, MotherDuck, or ClickHouse). (preferred)
12. Background in applied machine learning, natural language processing, and large language models. (preferred)
13. Working knowledge of Stata and SurveyCTO for research support, and of additional analytical software such as R. (preferred)
14. Experience with data engineering tools for ELT workflows such as dlt, SQLMesh, or dbt. (preferred)
15. Experience with geospatial analysis. (preferred)
16. Familiarity with research quality standards and best practices in survey data collection and data management. (preferred)
17. Experience with project management tools such as Jira or Asana. (preferred)
18. Experience working with international development organizations or research institutions. (preferred)
19. Ability to work in one or more of the following languages: Spanish, French, or Swahili. (preferred)