Associate Consultant
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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 28.000
- insurance coverages
- supplementary pension plan
- facultative health assistance
- flexible benefits
- 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
- 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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