MSF Sweden Innovation Unit: Data Analyst Consultant
Contract period: 3 months.
Expected time allocation: 50 – 75% (75-110H/month)
Expected start: June 2025
Background
Médecins Sans Frontières (MSF) is an international, independent, medical humanitarian organisation that delivers emergency aid to people affected by armed conflict, epidemics, natural disasters, and exclusion from healthcare. These programmes are run through five operational centres. Sweden Innovation Unit (SIU), MSF Sweden support our operations through innovation and research projects with the aim to improve our health programming.
In 2024 SIU launched a digital therapeutics mobile phone app (DTx), tested with an initial pilot cohort of 15 patients. Based on promising results from the initial pilot the DTx app will further scale up across two MSF clinics in Lebanon in Q2 2025, followed by further roll-out in additional project contexts from late 2025. As was done during the pilot, the metadata generated during usage of the DTx app is intended to continue to inform strategic decisions on the app’s development and usage by the SIU team in addition to being used in upcoming operational research on the Lebanon implementation.
STATA was used for the initial analysis of the meta- data, specifically looking at patterns in app usage. However, this approach is likely not fit for purpose as the user numbers and metadata points expand. Therefore, the SIU is seeking a data analyst to support the exploration, definition and development of a new, scalable approach to manage and analyse data generated by the DTx app, to analyse app adoption, user engagement, operational efficiency, and functional performance, and more.
Specifics on the first metadata analysis
The app metadata were examined in-depth to generate metrics of user engagement with the app, both overall and focusing on selected key features. Selected metrics aimed to quantify patterns of user interactions and preferences, examine app functionality, identify any potential areas requiring improvement, and gain an overall insight of the app’s relevance and value to users. Broadly, the metrics included elements of: number of interactions; number of conversions (interactions that resulted in a discrete measure of change e.g. saving blood glucose levels; setting a goal; or completing a learning module); frequency and duration of sessions overall and stratified by key features; interactions with notifications and nudges; and patterns of interactions suggesting issues with app design.
Analyses included data from the 15 participants included in the pilot phase. The main metadata consisted of timestamped and itemized interactions of each participant with the app, marking their journey of use across all sessions until 1st October 2024 – separated by the key section of the app. These were collated into a single dataset to create a consolidated view of all of each participant’s interactions with the app. This dataset was then used to summarize the metrics of interest. For individual metrics, such as duration of sessions, we calculated each metric per person and then calculated the average value across participants. Average values reported were either the crude median (interquartile range), crude mean (standard deviation), or mean adjusted for time (95% confidence intervals [95% CI]). Summary categorical metrics such as (such as setting of at least one goal on the app) were calculated as proportions (%) out of the total sample (N=15). In additional exploratory analyses, we stratified certain metrics by available participant metrics to examine potential explanatory factors for the patterns of interactions and engagement observed for the different app features. To enable this, a dataset containing basic participant sociodemographic characteristics (include age, type of diabetes, and other relevant characteristics such as access to a glucometer or continuous glucose monitoring) was merged with the app metadata. Key metrics were then compared between relevant participant groups. Statistical tests were used to assess significance in differences between participant groups. These included Student’s T test or Wilcoxon rank sum tests for continuous metrics, and Fisher’s exact tests for categorical metrics. Any comparisons between subgroups were examined using Student’s T test or Wilcoxon rank sum test (continuous variables), or Fisher’s exact tests.
Finally, the participant-level data and metadata described above were supplemented with supplementary measures as extracted from the app development platform, including indices such as app crashes and server uptime.
The Assessment of current need
As alluded to above the next phase of this project is projected to require a data analyst with the ability to build on what has been done so far and reconceptualise what is required from a metadata management and analysis perspective going forward in addition to leading ongoing analysis based on raw metadata exports. The team is seeking an individual with experience and expertise in the following areas:
Handling Mobile Health Application Data
- Experience working with large volumes of data. DTx data anticipated to reach 500+ users by end of 2025 and 1000s thereafter with some potential increase in required variables captured.
- Demonstrated ability to scale data systems over time with changing demands.
Tools and Technologies
- Proficiency in SQL and other relevant tools used for data cleaning and analysis.
- Experience building and maintaining data pipelines including periodic updates.
- Experience developing dynamic dashboards to visualize and communicate key insights derived from data.
Reporting & Automation
- Ability to automate reporting processes.
- Experience generating reports in formats usable by both clinicians and patients, including automated notifications based on data analysis.
Data Analysis Scope
- Skilled in analysing both raw health data and metadata related to app engagement from multiple sources (e.g. logins, feature usage, crash reports, demographic data).
- Ability to design and track user engagement metrics for mobile health platforms.
Data Management and Integrity
- Experience with data storage, version control, and security protocols.
- Ability to troubleshoot data issues, including logging and resolving cleaning problems and developing or contributing to systems that flag data anomalies or quality issues.
Profile of consultant(s):
Essential skills:
- Bachelor’s or Master’s degree in a relevant field (Data Science, Statistics, Information Systems, Public Health with a quantitative focus or Health Informatics)
- Proficiency in SQL for querying, data extraction, and transformation
- Advanced data analysis skills in Python or R (or similar), including use of libraries for statistical analysis and data visualization
- Experience managing and analysing large, complex datasets, such as app user metadata
- Understanding of health data sensitivity and ethical data handling, including compliance with data protection policies
- Proven skills and experience in supporting remotely located teams
- Strong analytical and problem-solving skills.
- A good understanding and knowledge of medical issues and challenges in humanitarian contexts.
- Experience working in public health, NGOs and/or the social sector.
- Fluent in English written and spoken.
Desirable skills:
- Familiarity with mobile app telemetry data, including events, interactions, crashes, and session metrics
- Experience working with digital health / mHealth or public health projects, ideally in humanitarian or LMIC contexts
- MSF (or equivalent) experience.
- Knowledge and working proficiency of Microsoft 365 environment including Teams, SharePoint and Power BI.
How to Apply:
To be submitted:
- Your CV and motivation letter
- Examples of previous work – provide a summary of similar work that you have developed and delivered before.
- Fee (hourly rate). Include NGO discount if applicable.
Deadline: Midnight 27th May 2025
To submit your application or if you have any questions email natalie.oconnell@stockholm.msf.org
