Fabio Concina

Head of Analytics & AI at kwantis

About Me

Engineering leader with 10+ years in software and data, currently leading an 8-person Analytics & AI team at kwantis with end-to-end ownership of a core product area. Accountable for roadmap delivery, people management, and production adoption of AI capabilities.

Experience

Head of Analytics & AI

kwantis | 2025 - Present

  • Lead an 8-person Analytics & AI engineering team with end-to-end ownership of a core product area.
  • Own roadmap execution and delivery for AI-driven features in the ID3 product, in close collaboration with Product and Business stakeholders.
  • Accountable for team performance, hiring, onboarding, and professional growth.
  • Delivered LLM-enabled and agentic AI capabilities into a production SaaS product used by enterprise customers.

Manager, R&D

kwantis | 2020 - 2025

  • Managed the R&D division and helped grow the engineering team.
  • Led the replatforming of ID3's core analytics module to an embedded Tableau-based architecture, with a custom wrapper enabling seamless integration into the product.

Software Engineer

kwantis | 2015 - 2020

  • Core contributor to the design and development of the ID3 (id3.ai) product, with a focus on architecture and early technical direction.

Publications

Detecting Low-Quality Intervals in Surface Logging Data Using AI-Based Anomaly Detection

Alireza Nadirkhanlou, Fabio Concina

SPE Europe Energy Conference and Exhibition, Vienna, Austria, June 2025

Advanced Analytic Tool Development for GHG Emissions Monitoring in Well Operations

Vanessa Silvia Iorio, Daniele Farina, Stefano Racca, Luca Dal Forno, Fabio Concina

Offshore Technology Conference, Houston, Texas, USA, May 2023

Big Data for Advanced Well Engineering Holds Strong Potential To Optimize Drilling Costs

Nicola Rossi, Jean Michelez, Fabio Concina

Journal of Petroleum Technology, Vol. 70, No. 02, pp. 18-21, February 2018

Education

MSc in Statistics and Actuarial Science

University of Trieste, Trieste, Italy

Contributed to the R ChainLadder package as part of thesis research on stochastic reserving methods.

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