Data and Behavioral Scientist
Organizational Context
The CEB Secretariat, within the UN Secretariat, focuses on Inter-Agency Human Resources matters. This role supports the Senior Inter-Agency Human Resources Advisor by developing data and analytics-driven solutions for HR issues, including demographics, costs, and behavioral aspects. It also involves generating and communicating data-driven insights to aid decision-making processes.
Job Purpose
The Data and Behavioral Scientist will identify, structure, and implement data and analytics-driven solutions for Inter-Agency Human Resources matters, ensuring a strong conceptual and scientific foundation for objectivity and credibility. The role involves generating and communicating HR-related data-driven analyses and insights to support decision-making by examining historical trends, current data, and predictions. Key responsibilities include collaborating with stakeholders to leverage data for business solutions, developing and implementing data systems and analytics strategies, applying causal inference and experimental methods, collecting and analyzing diverse datasets, assessing data quality, developing predictive algorithms, and coordinating model implementation and monitoring across CEB organizations.
Responsibilities
Collaborate with stakeholders across CEB organizations to identify opportunities for data-driven business solutions. Develop and implement databases, data collection systems, and analytics strategies to optimize statistical efficiency and quality, including automation scripts and internal software. Apply causal inference and experimental methods (e.g., randomized controlled trials, quasi-experimental designs) to estimate policy impacts and inform evidence-based decisions. Collect, mine, scrape, and analyze data to enhance inter-agency coordination in behavioral sciences, human resources, organizational management, and compensation. Assess and test the effectiveness and accuracy of new datasets and data processing procedures. Develop custom predictive algorithms and coordinate with functional teams to implement models and monitor outcomes.
Work Experience
A minimum of five years of progressive experience in data science, analytics, applied mathematics, or information management is required. Experience combining data analytics with behavioral science is essential. Proficiency in data science programming languages (R, Python), SQL, and relational database management is mandatory. Desirable experience includes data governance, data quality, privacy/security practices, JavaScript, web frameworks, Git, Docker, API development, data integration, people analytics, workforce planning, or compensation analysis.
Skills
Data analysis life cycle knowledge (ingest, wrangling, analysis, visualization). Expertise in statistical and computational methods (clustering, classification, correlation, forecasting, machine learning, regression). Ability to apply judgment, plan work, and manage priorities. Strong organizational and planning skills. Accountability and commitment to delivering results. Proficiency in R, Python, and SQL. Experience with data analytics and behavioral science. Causal inference and experimental methods.