Panzo Gonga

Within the Leak Division's design office with several engaged teams (software, electronics,

mechanical, and R&D), I participate in the development of new ATEQ device series (S7) as an Embedded Software Engineer:

Design and Development:

• Design embedded systems according to specifications.

• Develop software for embedded microcontrollers like STM32.

Programming:

• Write code using C, C++, and Python languages.

• Implement algorithms specific to embedded systems.

Testing and Validation:

• Perform unit and integration tests to ensure the software functions as expected.

• Debug and resolve code issues.

• Update software to fix bugs or add new features.

Collaboration:

• Work with multidisciplinary teams (hardware, firmware, etc.).

• Participate in project meetings to discuss progress and challenges.

Research and Development:

• Explore new technologies and tools to improve development processes.

• Participate in research on innovative concepts for new features.

Technological Environment:

Embedded: C++, STM32 h7xx, STM32CubeProgrammer, STM32CubeIDE, TouchGFX Designer, Grafcet yEd

PC: Python, ModBus Rtu , GitLab.

As a teacher, I taught Master's students (Year 1 and 2) in Artificial Intelligence the deep learning module:

Basic concepts and algorithms of deep learning (RNN, CNN, GAN…)

Framing of various deep learning projects: NLP, Image Processing.

As a Transverse Data Marketing and Performance Management Officer, my main missions consisted of analyzing and exploiting data to generate business: GRDF has a significant amount of data, and within the framework of publicly available data, I implemented analyses of this data to identify development opportunities. Exploiting data to perform analyses of projects and the performance of marketing and sales team actions.

As well as Data Project Management and the development of a marketing and sales reporting tool.

Therefore;

1. Data Project Management:

Data Exploration, Modeling, and Valorization.

1.1. Exploration:

Collect data from the OpenData site and existing data.

Use of Webscraping (collecting specific data on potential clients).

Data visualization and cleaning.

1.2. Modeling:

Development and deployment of machine learning algorithms.

1.3. Valorization: Management, Sharing, and Accessibility.

Management: Develop the company's transverse capacity to manage data.

Sharing: Implement policies governing access throughout the data lifecycle.

Accessibility: Adapt the availability and presentation of information to meet different exchange needs.

2. Technical support for marketing and sales teams:

Management of marketing and sales projects.

Development of marketing and sales reporting tools from data.

Data analysis and consulting / training on data tools...