About Us

Flowminder is a non-profit foundation that specialises in the analysis of anonymised mobile phone data, satellite imagery, and household survey data for humanitarian and international development purposes.

Our mission is to enable decision-makers to access the data they need to transform the lives of vulnerable people, at scale.

Flowminder researchers pioneered the use of mobile operator data for humanitarian purposes and were the first to use mobile operator data to monitor population movements following a large-scale disaster and a major infectious disease outbreak (the Haiti earthquake and cholera outbreak, 2010). Also, Flowminder was the first to show that mobile operator data can predict the spatial spread of an infectious disease.

We work with governments in low and middle-income countries, UN agencies, The World Bank and other development banks.

We are looking for a data scientist - applied mathematician for analysing geo-referenced time series extracted from mobile operator data, for the purpose of measuring and understanding human mobility to support the humanitarian and development sectors.

The Role

Data scientist is a senior role that entails data analysis and code development, as well as project design and business development.

As a data analyst (applied mathematician) you will develop and test new methodologies for processing, analysing and interpreting large spatio-temporal datasets for the development and humanitarian sectors.

As a developer, you will contribute to writing queries and code to extract and process data from medium- to large-size databases in a memory-efficient manner and will ensure sensitive data remain secure at all times.

As a senior team member, you will engage with end-users to understand their needs and identify which insights will be most relevant, as well as with donors to support business development.

Main tasks will include:

  • Conducting exploratory and standard analysis of time series, geo-referenced time series, images and maps, and geo-referenced survey data
  • Developing novel data analysis methods and algorithms, data metrics, statistical and mechanistic models
  • Anomaly detection and data cleaning
  • Data integration: combining multiple data sources in analyses (spatial data, survey data, mobile operator data)
  • Assessing and quantifying data limitations and uncertainty of outputs
  • Protecting subjects’ privacy, including acting in accordance with GDPR

We seek talented and devoted data scientists/applied mathematicians who look to apply their skills to support current humanitarian and development efforts and seek to provide accessible and data-driven solutions to inform decision-making in LMICs. Our focus is to implement and operationalise the high-quality data products and solutions that we, and occasionally others, develop.

In addition to scientific work, experienced candidates will have the chance to lead small to medium-sized projects or act as the project director for certain initiatives.

About You

Your motivation to work with us should stem from your interest in tackling the following problems:

  • How to improve information extraction from mobile phone usage data (Call Detail Records, CDRs)
  • How to identify and characterise the patterns of normal human mobility and detect abnormal changes
  • How to ensure data products are computed in an efficient, robust and timely manner
  • How to support the humanitarian and development sectors to make the most of mobile operator data

Essential criteria

  • A PhD in applied research in a quantitative discipline. Fields of expertise may include signal processing, time series analysis, data mining, pattern recognition, image processing; and areas of application could be any sector that uses time series
  • Strong ability to reason under uncertainty: combining quantitative analytical thinking with contextual knowledge and using critical thinking to extract and interpret information and insights from data
  • Demonstrable experience in the exploratory analysis of time series, collected outside of experimentally controlled environments
  • Experience with sparse and irregular time series
  • Experience with spatial data and/or geo-referenced data
  • Proficiency in, and at least two years’ experience of, Python and its data analysis libraries, such as Pandas, NumPy, sklearn and Bokeh/Matplotlib
  • Experience in writing well-documented, maintainable code
  • Excellent written and verbal communication skills in English
  • A genuine desire to see the outputs of your work improve the lives of vulnerable populations

Other organisations may call this role Mobile Data Scientist, Mid-Level Data Scientist, Senior Data Scientist, Lead Data Scientist, Data Analyst, Insights Analyst, Statistician, Mobile Data Analyst or Project design Analyst.

The role may correspond to post-doctoral research associate in academia or principal investigator for more experienced candidates.

The Benefits

  • Salary of up to £57,900 per annum
  • 37.5 hours per week
  • Flexible working hours
  • Support projects to improve the lives of the world’s most vulnerable
  • The opportunity to gain knowledge in various fields (such as public health, emergency coordination, disaster management, public services management) directly from experts in many countries
  • The opportunity to work on and explore large-scale and near-real-time datasets reflecting human mobility
  • Traveling is common for many data scientists and part of the role for project leaders in order to develop an understanding of the context and needs of stakeholders
  • Collaborate with a talented and passionate team

To Apply

Applicants should email careers+da0721@flowminder.org with the following (preferably in pdf format):

  • A brief expression of interest outlining why you are interested in being part of the work
  • CV(s) (2 pages) with details of relevant experience

Please note that due to the very high volume of applications we receive, we greatly regret that we are unable to send personalised acknowledgements or give feedback on applications. Only applications following the requested application format will be considered.

 

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