Daniele Conti Email and Phone Number

• Led a large-scale global initiative to enhance Enel’s Data and AI governance, driving operational efficiency, innovation, and compliance with European regulations.• Directed a critical project to forecast energy demand, achieving a 5% reduction in forecasting errors for ~12 million customers, enhancing risk management and operational efficiency.• Collaborated on developing a generative AI model for efficient information retrieval in company technical documentation.• Authored corporate policies for managing AI-related processes, ensuring alignment with European regulatory standards and robust risk management.• Led the technology scouting team for the new enterprise Data Catalog tool, establishing technical governance requirements.• Utilized PySpark to analyze ~4 billion of consumption records, enabling faster and more accurate insights for strategic decision-making.• Oversaw a team of two data scientists and a data engineer, successfully developing predictive models for EBITDA components, improvingaccuracy by 10% and facilitating better financial forecasting and decision-making.• Designed, implemented, and deployed a forecasting model with <1% error to predict maintenance costs, leading to annual cost savingsexceeding $1 million.• Spearheaded a data-driven newsletter, curating and disseminating insightful articles on data-driven projects and emerging technologies,fostering knowledge sharing among stakeholders and promoting a culture of innovation.• Developed an AI system using Camelot to optimize control room operations, resulting in a 20% increase in operational efficiency and enhancedrisk mitigation capabilities.• Utilized clustering techniques and a recommendation system to create a new dunning action model, significantly improving debt collectionefficiency and reducing financial risk.• Utilized SQL queries to build a comprehensive and accurate global customer database.

Research project: "Non-equilibrium dynamics of adaptation in sensory systems".• Developed a hybrid C++ and Python script to perform Bayesian inference of stochastic processes implementing Ito calculus, Fokker-Planck equation and Linear Response Theory• Performed a time series analysis of the stochastic dynamics using regression and correlation methods in Python with NumPy and Pandas• Developed an analytical model to represent sensory adaptation and performed an extraction of the relevant dynamic properties (timescale, accuracy, information transmission) along with back-testing simulations (publication)• Presented results in three international conferences and wrote scientific papers along with cover letters for top peer-reviewed journals

Research project: "Signal Propagation and Dynamic Correlationsin Biological Active Matter".• Implemented a dynamic scaling analysis of correlation functions showing criticality in natural swarms of midges which resulted in a publication chosen as cover on Nature Physics • Analyzed and modeled large data sets of bird flocks and midges’ swarms using ML techniques and Statistical Analysis such as dimensionality reduction and correlation with C++ • Developed, implemented, and tested a new quantitative method distinguishing different dynamics of collective motions (publication) with a C++ script simulating Vicseck models in 3D• Conceptualized and performed analytical analysis and back-testing simulations in C++ of a model characterizing the propagation of speed fluctuations within highly polarized systems (publication)