Senior Consultant, Geospatial Data Science

Our Data Science team applies modern methods and innovative technology to some of the most pressing challenges in international development. Working across the data and policy cycles, we combine sector expertise, creative technology and a deep understanding of behaviour change and organisational reform to turn complex data into practical insights. The team sits within OPM’s wider Quantitative Methods and Data Science technical area, within the Research and Evidence Practice, and works alongside specialists in quantitative impact evaluation, primary data collection, third-party monitoring, and monitoring, evaluation and learning systems.  

As a Senior Consultant in Geospatial Data Science, you will help shape and deliver OPM’s approach in applying data science—particularly geospatial analysis—to research and policy evaluation. You will develop tools that enable policymakers to understand not only what is happening, but where, so they can design better-targeted solutions. Utilising the latest technology to fuse traditional and non-traditional quantitative research methods that examine how public policy initiatives strengthen resilience, your work will help governments and institutions direct resources towards communities with the greatest need.  With a robust understanding of geospatial mapping and analyses (including Small Area Estimation techniques), the Senior Consultant, Geospatial Data Science will hold primary responsibility for developing this area of our work. 

Working in close collaboration with a diverse range of multi-disciplinary teams globally, the Senior Consultant, Geospatial Data Science brings an innovative and independent approach to problem solving and the ability to effectively build relationships with clients and other external partners. They support the wider team in the identification of new business opportunities, design high-quality proposals to win impactful work, and build effective internal relationships that support other OPM colleagues in developing their data science skills and expertise. 

• An advanced degree in a numerate discipline (data science, computer science, statistics, economics etc.) or equivalent professional experience.

• At least 7 years’ experience of working on social research and/or policy analysis within LMIC settings.

• High level understanding of the data landscape in LMICs, including traditional and non-traditional data.

• Exceptional skills in R. (Strong knowledge of Stata, QGIS, Python is a plus.)

• Advanced understanding of analytical pipelines, especially using R (and/or Python) and related spatial packages to automate geospatial data processing tasks.

• Substantive experience in the application of innovative data science techniques, in particular geospatial modelling (e.g. Bayesian frameworks using INLA, timeseries analysis for anomaly detection).

• Experience in developing and delivering Small Area Estimation (using both survey and census as well as other geospatial data) projects.

• Experience with cloud computing platforms, such as Google Earth Engine, AWS, Google Cloud Platform, and big data processing tools for geospatial analysis (e.g. Dask and Xarray).

• Authentic passion for developing new techniques, and for working with interdisciplinary teams to adopt and deploy them.

• Excellent attention to detail and analytical rigour.

• Switches comfortably between detail and the bigger picture, joined up thinking and approach to providing solutions.

• Good at prioritising changing and competing demands and tasks, while consistently delivering quality work.

• Excellent problem solving, organising and management skills.

• Strong interpersonal and communication skills; collaborative and constructive approach to working with others, builds effective relationships internally and externally.

• Experience in translating complex spatial analyses into clear visualisations for technical and non-technical audiences.

Desirable Qualifications/Experience/Skills/Behaviours  

• Experience in remote sensing and earth observation: Processing and analyzing satellite data (e.g. Sentinel, Landsat) for environmental monitoring, land-use dynamics, and ecosystem health.

• Experience with big data handling, parallel computing, and working on the cloud in an Azure environment.

• Further skills in other quantitative methodologies of interest to the team (e.g. quantitative impact evaluation methods, AI for evaluation).

• Fluency in languages additional to English.