Data Scientist

Responsibilities

Implementation Research

• Design implementation research protocols and lead studies with HQ departments and country teams, from sampling and study design through data analysis and dissemination.
• Ensure studies meet ethical review (IRB) and data protection requirements, including for data collected from children and other vulnerable groups.
• Lead or support country teams in analyzing study and program data using advanced statistical methods (e.g., regression, mixed-effects and Bayesian models) and, where appropriate, machine learning and AI-assisted tools.
• Lead the writing of reports, policy briefs, blogs, and peer-reviewed publications, and represent Pure Earth at scientific and technical conferences and meetings.

Data Science Capacity and Learning

• Build staff skills in data analysis and data use so that teams can analyze the program data they collect.
• Design and facilitate learning mechanisms (e.g., workshops, learning events) that promote cross-team learning, reflection, and decision-making, and train teams to use them.
• Document lessons learned and co-develop knowledge products that assess current data collection protocols and approaches, guide program strategy, communicate findings, and contribute to the sector-wide evidence base.
• Make complex data and findings accessible to non-technical internal and external audiences.
  Manage, analyze, and communicate both quantitative and qualitative data for program learning, strategy, fundraising, and communications.

MEL Systems and Project Design

• Advise on the continued development of Pure Earth’s MEL systems, processes, and tools so they capture progress, results, and lessons learned across major new projects.
• Support the development of proposals, concept notes, and funder-compliant project MEL plans aligned with Pure Earth’s program logic.
• Support efficient data collection for project and global indicators, including training staff on data collection tools and methods.
• Coach and train remote and distributed teams.

Success in the First 6 Months:

• Has made substantive contributions to design of organizational Management Information Systems (MIS) for MEL data.
• Has supported coaching and training of in-country staff on implementation research and programmatic data analysis.
• Has led development of 1–3 implementation research protocols.
• Has supported development of learning agendas and evaluation priorities

Skillset

Education

• Degree in public health, epidemiology, statistics, data science, or a related field, with one of the following: PhD and 6 years of relevant experience; master’s degree and 8 years; or bachelor’s degree and 10 years (or an equivalent combination of education and experience).

Experience

• At least 5 years designing and running implementation research or evaluation studies. and analyzing real-world program data, preferably in the international nonprofit sector.
  Advanced statistical skills, including sampling and study design, regression, and mixed-effects or Bayesian modeling.
• Proficiency in R, Stata, and/or Python, including in reproducible workflows and data visualization.
  Experience with research ethics, IRB submissions, and data protection.
  Ability to manage multiple concurrent priorities and deadlines.
  Able to meet teams where they are and recommend practical data science approaches that reflect realities of staff, partners, and other stakeholders.
• Have designed lean, rigorous implementation research strategies as part of an organization’s MEL strategy, ideally at an international NGO.
• You have built collaborative learning practices that turn knowledge from across departments into program improvements.

Desired (nice to have)

• Fluency in Spanish, Swahili, or another language used in countries where we work.
• Experience with environmental health or lead exposure data (e.g., blood-lead level surveys).
• Experience with geospatial analysis (e.g., QGIS, ArcGIS) or national health information systems (e.g., DHIS2).
• A record of peer-reviewed publications.