Data and AI Analyst (Combating Drug Trafficking and Organized Crime)

La Oficina de las Naciones Unidas contra la Droga y el Delito (UNODC) lidera a nivel global la lucha contra las drogas ilícitas, el crimen y el terrorismo.

En el marco de las Metas de Desarrollo Sostenible y bajo el enfoque de los Derechos Humanos, el objetivo primordial de UNODC es contribuir con los Estados a enfrentar las amenazas que ponen en riesgo la gobernabilidad, la estabilidad social y atentan contra las condiciones básicas necesarias para avanzar en el desarrollo humano de nuestros pueblos.

Organized crime in Peru manifests in complex and interconnected ways. Drug trafficking, illegal mining, money laundering, and environmental crimes are deeply intertwined, reinforcing one another and expanding their networks across borders. Gaps in state presence and pervasive corruption weaken institutions and enable illicit economies to thrive, posing a direct threat to the country's health, security, governance, and environment. Criminal networks operate in a dynamic and sophisticated way, that require integrated responses to strengthen the national responses to counter and effectively counter the threat they pose.

For this reason, adopting technologies such as data science and artificial intelligence is both a strategic and urgent imperative. These tools support a shift from reactive responses to proactive and predictive models, enabling the analysis of massive volumes of data to identify patterns, anticipate criminal hotspots, and map entire networks—including financial and logistical nodes. By applying such technologies, security strategies become evidence-based and operational actions more precise, ultimately enhancing policy formulation and improving the effectiveness of anti-crime interventions.

Under the direct supervision of the Data Analysis and Research Project Coordinator, the UN Volunteer will perform the following tasks:

Data Management and Consolidation
Identify, collect, and centralize relevant data sources, ensuring their quality, integrity, and security.

Analysis and Investigation
Apply statistical and modelling techniques to identify patterns, trends, and anomalies in illicit activities.
Use social network analysis (SNA) to map and understand key actors within criminal organizations.

AI Model Development and Implementation
Design, train, and implement machine learning models for predictive and prescriptive purposes.
Classification models: Use natural language processing (NLP) to automatically analyse thousands of text reports and classify them by crime type, modus operandi, or threat level.

Generation of Actionable Intelligence
Develop interactive dashboards and visualizations to allow operational leaders to consume information clearly and easily.
Prepare evidence-based strategic reports that recommend resource optimization, operational planning, and procedural improvements.

Capacity Building
Collaborate directly with investigative and operational units to understand their needs and ensure data products are relevant and useful.
Train other analysts and law enforcement personnel in the fundamentals of data analysis to foster an evidence-based culture.


Furthermore, UN Volunteers are required to:
• Strengthen their knowledge and understanding of the concept of volunteerism by reading relevant UNV and external publications and take active part in UNV activities (for in-stance in events that mark International Volunteer Day);
• Be acquainted with and build on traditional and/or local forms of volunteerism in the host country;
• Reflect on the type and quality of voluntary action that they are undertaking, including participation in ongoing reflection activities;
• Contribute articles/write-ups on field experiences and submit them for UNV publica-tions/websites, newsletters, press releases, etc.;
• Assist with the UNV Buddy Programme for newly-arrived UN Volunteers;
• Promote or advise local groups in the use of online volunteering, or encourage relevant local individuals and organizations to use the UNV Online Volunteering service whenever technically possible.