17 set - Latina
Marsh & Mclennan Companies
Overview
Join Marsh McLennan Risk Advanced Technology in Rome as part of the Professional Graduate Programme. You will apply ML, AI, and analytics to risk phenomena across domains, supporting credit and operational risk modeling and risk management transformation. You'll work with cross-functional teams to deliver data-driven insights and automation, shaping innovative risk solutions for public and private clients. This internship offers hands-on experience with senior professionals on real projects and a pathway to develop technical and consulting capabilities.
Retribuzione / Benefits
apprenticeship contract
gross annual salary starting from ******
insurance coverages
supplementary pension plan
facultative health assistance
flexible benefits
Responsabilità
Support ML/AI application to analyze risk phenomena and detect anomalies across domains
Contribute to quantitative and financial models in credit risk (PD, LGD, EAD, ECL)
Perform data analysis, dynamic reporting, risk monitoring dashboards, and early warning systems
Assist clients in developing and evolving risk management frameworks and digital transformation processes
Collaborate on developing new solutions and methodologies (Generative AI,
explainable ML, automation)
Participate in project phases: requirements, analysis, modeling, implementation, testing, go-live
Prepare deliverables and presentations for clients, ensuring clear communication of results
Interact with clients and stakeholders with team support to present analytical evidence
Requisiti fondamentali
Master's degree or higher in a STEM-related field with excellent academic record
1–2 years of experience in consulting, data analytics, risk management, or related areas
Interest or foundation in data analysis, ML/AI, credit risk modeling, and BI tooling
Strong Python skills (pandas, scikit-learn) and basic SQL
Familiarity with Power BI and Power Platform is a plus
Strong analytical, documentation, and presentation abilities
Excellent interpersonal and stakeholder engagement skills
strong analytical skills
attention to detail
collaborative mindset
Data analysis using BI tools (Power BI)
Machine Learning basics (classification, regression, clustering, anomaly detection)
Credit risk modeling concepts (PD, LGD, EAD, ECL)
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