How we work
Our team spans 20+ countries and 69 languages. Our culture is candid, analytical, and non-hierarchical. We expect high ownership and give people real responsibility early. Our values are central to how we hire, work, and grow, and they are deliberately opinionated:
Recipients first. Recipients’ preferences come before those of donors or ourselves.
Team next. We optimize for organizational success, not individual success.
Be proactively candid. We say what we believe and share information openly.
Create positive energy. We aim to be a source of energy for colleagues, not a drain.
Think rigorously; act quickly. We reason from first principles, then act rather than debate.
Know yourself and grow. We own our imperfections and seek feedback to grow.
Accept reality. Propose solutions. We focus on what can change and bring solutions, not just problems.
Be productively ambitious. We take risks for step-change impact, not incremental progress.
If they describe how you like to work and they energize you, this could be a great fit.
Want to learn more? Read and listen
We are one of Time100’s Most Influential Companies of 2026, and our work has been covered by The Economist, NPR, TED, and The Washington Post. We’re now preparing to deliver billions of dollars a year to people in extreme poverty, if funding allows. Read our CEO’s essay on what that will take.
About this role
GiveDirectly gives cash directly to people living in poverty and in emergencies. Both depend on the same question: did this person live in this place, at this time? We ask it when we target the poorest households, and when a disaster displaces a community overnight. Location records held by Mobile Network Operators (MNO) can help us estimate an answer to this question at scale. We work with MNOs so they can infer, from their own records, whether a subscriber was resident in a target area over a defined period. Robust residence location inference from MNO data requires in-depth analytical work, and the methods we design have to run on an operator’s systems – most often implemented by their engineers – under strict data protection and confidentiality rules.
We are hiring a Senior Data Scientist to progress our work on location inference from MNO data, and to make it repeatable. Your job is to improve and extend our library of method templates, adapt them to each new operator and setting, and design new elements when an intervention requires it. You’ll assess what can be inferred from a given operator’s data, specify the analysis clearly enough for their data engineers to implement, then test it with them remotely or on site. You’ll support introductory meetings with MNOs, describing methodologies at a high level, and inform the work of data privacy and cybersecurity teams to ensure only necessary data are shared securely. You’ll also help colleagues across GiveDirectly understand the strengths and limitations of this data source, so we design programs around questions it can best answer.
This work is live in several countries, and we’re expanding it across the countries where we have offices and to low- and middle-income countries anywhere in the world when a crisis hits.
Reports to: Senior Product Manager – MNO data
Level: Senior Manager
Location: At this stage of GiveDirectly’s growth and scale, we’re prioritizing hiring in the countries below, where we can employ you through a GD entity or an established employer of record: Cameroon, Canada, Colombia, DRC, Germany, Ghana, Kenya, Malawi, Mozambique, Netherlands, Nigeria, Poland, Portugal, Rwanda, South Africa, Spain, Turkey, Uganda, United Kingdom, and United States. This list reflects where our employment infrastructure is established today, and may evolve as we grow. If you’re based elsewhere and not open to relocation, we may not be able to consider your application for this role right now, but we’d encourage you to check back. Please note that we are unable to sponsor or take over sponsorship of employment Visas in the U.S. or U.K. at this time.
Travel Requirement: Substantial international travel, roughly 25 to 50% of your time depending on emergencies, sometimes at short notice – for workshops with operators to support programs and emergency responses.
What you’ll do
Improve and extend our library of analytical method templates for establishing where people live from MNO data, and depending on circumstances, for detecting displacement or estimating poverty. Design novel methods when an intervention requires it.
Support colleagues in technical conversations with operators, bringing clarity on the data we need and why, and steering discussions toward technical designs that make the analysis workable.
Assess the information that can be inferred from each operator’s data given data types and sparsity, and for each geography and targeted area. Support intervention design to account for data quality.
Write tests and method specifications for MNO data engineers to implement: steps, parameters, pseudocode, validation criteria.
Guide operator engineering teams through implementation, in writing and in person. Adapt methods to accommodate large data volume and computational constraints, balancing accuracy and operational needs.
Run in-country workshops with operators to support the set-up of new analyses and – where feasible – tune parameters based on statistical tests and distributions.
Feed every new analytical and implementation element back into standard templates, so the next country or intervention starts from a better position than the last.
Design for privacy from the start, so that as little data as possible has to be shared: aggregation, minimization, and where useful, privacy-enhancing technologies such as private set intersection or homomorphic encryption.
Protect the confidentiality of what operators share with us and improve internal data security processes and tools as we grow.
Build internal training material so that our technical and non-technical teams understand the strengths and limits of this data, and design interventions accordingly.
What you’ll bring
Exceptional alignment with GiveDirectly Values and active demonstration of our core competencies: emotional intelligence, problem solving, project management, follow-through, and fostering inclusivity. We welcome and strongly encourage applications from candidates who have personal or professional experience in the low-income and/or historically marginalized communities that we serve.
Strong mathematical modeling, machine learning and statistics. You choose methods for the question rather than for their sophistication, and you value being able to explain a result over squeezing out the last point of performance.
Deep comfort with spatio-temporal data: temporal filtering, GIS, probability distributions, combinatorial reasoning, and working with sparse, irregular observations. Direct experience with Call Detail Records (CDRs) or latching data is a strong plus. Comparable experience with mobility, transport, sensor or remote-sensing data counts.
The ability to reason without ground truth. Much of this work has no validated answer to check against, and you’re comfortable setting parameters by reading the shape of the data and defending that choice.
Pragmatism about the trade-off between accuracy and implementability. You’ve deliberately simplified a method to fit an operational constraint, and you can say what it cost you.
Excellent communication and relationship-building skills. You can explain a method and its limitations to an operator’s engineers, to their data protection officer, to their leadership and to colleagues with or without technical background. You write well and are comfortable presenting to these multiple audiences. You build trust with counterparts you don’t manage, and they want to work with you again.
Experience writing technical specifications that other teams implemented, clear enough that people you don’t manage could follow them correctly.
Able to read and review SQL and Python written by others, and to find the logical error in someone else’s query. Writing production-quality code yourself is welcome but not required. Enough understanding of large data volumes and databases to judge when analytical simplification is required.
Commitment to privacy by design as a foundational value and sta
