We are seeking a talented individual to join our Risk Advanced Technology team at Risk Consulting through our Professional Graduate Programme. This Full‑time onsite position will be based in Rome, LAZ, IT.
Responsibilities
- Support the application of Machine Learning and AI techniques to analyze risk phenomena across heterogeneous domains (e.g., financial risk, operational risk), detect anomalies/fraud, reduce processing times, and contribute to simplifying business processes with a view to intelligent automation.
- Contribute to the development of quantitative and financial models, also in the Credit Risk area (e.g., PD, LGD, EAD, Pricing, stress testing, ECL), with the support of more senior professionals and in line with leading market best practices.
- Perform data analysis, dynamic reporting, risk monitoring dashboards, and early warning systems using Business Intelligence and Advanced Analytics tools.
- Support clients in developing and evolving risk management frameworks and digital transformation processes.
- Collaborate in the development of new solutions and methodologies (e.g., Generative AI applied to risk management, explainable ML models, process automation, advanced analytics).
- Take part in the various project phases: requirements gathering, functional analysis, modeling, implementation, testing, and go‑live support.
- Prepare deliverables, analyses, and project presentations, contributing to the quality of the work and the clarity of communication with the client.
- Interact with clients and project stakeholders, with team support, presenting analytical evidence and results clearly and effectively.
Requirements
- Master's degree or higher in Management, Management Engineering, Computer Science, Statistics, Financial Mathematics, or related STEM disciplines, with an excellent academic record.
- 1–2 years of experience in consulting, data analytics, risk management, model development, or related fields.
- Interest and/or solid foundations in one or
📌 Consultant - AI & Advanced Technology at Marsh (Roma)
🏢 Marsh
📍 Roma