Alain TAMDEM

- Developed machine learning models for cross-selling, upselling, and churn prediction, improving campaign targeting precision by an estimated 20–30%. - Built OCR and deep learning solutions automating legal document verification workflows, reducing manual validation workload by approximately 60–80%. - Designed and deployed an AI-powered recommendation and argumentation engine reducing advisor preparation time by an estimated 40–50%. - Built a RAG-powered enterprise chatbot reducing internal document search time by more than 80% through intelligent semantic retrieval across company knowledge bases. - Developed an AI-powered agent for life insurance statement analysis, beneficiary extraction, structured data storage, completion recommendations, and querying workflows, contributing to an estimated 30% increase in beneficiary subscription rates after customer death events. - Implemented Generative AI-based sentiment analysis pipelines processing thousands of customer feedback records and reducing manual review effort by more than 70%. - Delivered enterprise Power BI dashboards used by 50+ stakeholders across actuarial, finance, marketing, and customer experience teams. - Designed and implemented end-to-end Dataiku workflows to ingest raw data from the enterprise banking Data Lake, engineer features, and deploy cross-sell machine learning models and personalized insurance product recommendation engines, driving a 15%+ increase in insurance product uptake among banking customers.

- Developed customer segmentation and scoring models improving marketing campaign targeting quality and increasing campaign efficiency by an estimated 15–25%. - Engineered scalable PySpark and Spark SQL data pipelines to consolidate and transform large-scale datasets from 10+ enterprise data sources into a centralized analytics platform, enabling high-performance AI, machine learning, and business intelligence initiatives - Built OCR-based accident report processing solutions reducing manual processing workload by approximately 60%. - Delivered Qlik Sense dashboards reducing reporting preparation time from days to near real-time monitoring. - Trained 30+ business users on Dataiku self-service analytics workflows and low-complexity AI use cases. - Supported customer experience optimization initiatives through analytics solutions leveraging large-scale NPS and customer feedback datasets.

- Developed analytics tools supporting bond portfolio allocation and trading activity monitoring for investment and risk teams. - Built deep learning anomaly detection models for yield curve validation, reducing manual verification workload by more than 50%. - Improved operational monitoring efficiency through advanced analytics dashboards and automated financial validation processes. - Contributed to analytics initiatives supporting investment monitoring and risk analysis activities across international teams.

- Contributed to enterprise-scale telecommunications and digital banking software projects serving large customer environments. - Developed recommendation systems leveraging digital media consumption behavior to improve personalization capabilities. - Built machine learning fraud detection solutions improving monitoring efficiency for online digital content transactions. - Participated in backend software architecture, analytics implementation, and large-scale infrastructure monitoring projects.

- Leading the design and development of an AI-powered agent automating technical assessments for data and AI recruitment processes. - Building intelligent evaluation workflows combining LLMs, scoring systems, and automated candidate analysis to improve recruitment efficiency and assessment consistency. - Contributing to scalable AI recruitment solutions aimed at accelerating screening and improving technical talent identification.

github.com/AllanTamdem/buc_factory

Supporting educational success initiatives for teenagers from underserved communities