I’m a results-oriented, strategic leader who has spent years turning data analytics and business intelligence into AI/ML-driven solutions. I design and implement AI agents and intelligent automation using Python, Generative AI, Bot frameworks, and workflow automation tools to drive practical, high-impact outcomes across healthcare, insurance, consulting, and digital transformation projects.
I thrive on delivering short-term AI/ML wins, building end-to-end pipelines, dashboards, and predictive models. I’m fluent in English and German, comfortable working in international contexts, and passionate about applying Generative AI concepts to optimize business processes and automate complex tasks with tools like Botpress and Make.
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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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