In 2014, TTG, a small Australian company specialized in scientific algorithms, won a major energy optimization tender from SNCF, outperforming larger competitors like Siemens. However, the collaboration was at risk: while TTG had the best algorithmic engine, the project was lacking in delivery, management, and integration capabilities.
I was brought in as interim CTO for the project. I combined my expertise in scientific computing and iOS development to lead both the integration of the core algorithm and the development of the onboard system for iPad. I also coordinated with the University of Adelaide, which was responsible for the algorithm's evolution.
On the technical side, I developed the interface between C/C++ scientific code to Objective C and Swift, designing the APIs for the SNCF developers group. I also adapted the power hungry optimisation algorithms to the very ressource restricted embedded devices used on the Bombardier/RENFE trains. It allowed to use the algorithms without having to go through the several years long certification process required by any changes on the Bombarder embedded automatic driving systems.
On the management side, a major challenge was the internal resistance within SNCF. Thanks to a combination of technical leadership and soft skills, I managed to rebuild trust with key stakeholders, realign the teams, and bring the project back on track.
The POC deployed on the Paris–Marseille line achieved the targeted 10% energy savings. Nine months after my arrival, TTG signed a €1 million license agreement with SNCF, opening the door to a 7-year collaboration.
Key strengths: Scientific computing, mobile integration, energy optimization, team leadership, project recovery