I’m Corrado Centrone, a results-driven leader with a track record of transitioning data analytics into AI/ML-powered solutions. I design and deploy AI agents and intelligent automation using Python, Generative AI, Bot frameworks, and workflow automation tools. My industry experience spans healthcare, insurance, consulting, and digital transformation, with a focus on delivering high-impact, short-term AI/ML initiatives such as predictive analytics, conversational bots, and end-to-end automation pipelines.
I specialize in building scalable data platforms, ETL processes, dashboards, and end-to-end ML pipelines (including Healthcare risk modeling). I’ve led teams, established data analytics divisions, and implemented AI-driven processes like advanced BI, automated quoting, and document automation. Fluent in English and German, I work effectively in international contexts.
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Context: The need to respond quickly to inbound leads and improve commercial engagement rates.
Objective: To automate the first contact with prospects and initiate a structured conversation via WhatsApp.
Key Activities: Design of the marketing automation flow
Integration between Make, Twilio, and WhatsApp
Automated responses and guided conversations
Lead qualification prior to human interaction
Outcome / Value: Increased engagement, faster commercial response, and optimized use of the sales team’s time.
Context: Customers struggled to estimate the quantity and type of documents to be digitized, slowing down the quotation process.
Objective: To automate quotation generation, including estimates derived from user-uploaded images.
Key Activities: Design of a conversational chatbot
Automated image analysis
Estimation of document quantities and classification
Automatic generation of quotations
Outcome / Value: Reduced response times and increased scalability of the sales process.
Context: A healthcare fund with a significant historical dataset of dental claims needed to improve its ability to forecast future costs.
Objective: To analyze spending dynamics, predict future risk, and support pricing and long-term sustainability decisions for the healthcare plan.
Key Activities: Exploratory analysis of claims data
Feature engineering on demographic and historical data
Development of segmented predictive models
Creation of indicators and dashboards for management
Outcome / Value: Improved forecasting of future costs and enhanced control over insurance risk.
Context: a startup focused on burnout prevention and the improvement of organizational well-being through data-driven approaches.
Objective:To build a predictive model capable of analyzing questionnaire data and behavioral indicators, classifying burnout levels, and supporting preventive decisions for HR and management.
Key Activities: Dataset design and definition of relevant features
Development of supervised machine learning models
Model validation and results interpretation
Integration of the model into the platform’s product workflow
Outcome / Value: A scalable system for burnout risk classification, providing concrete decision support for organizations.
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