An energy data manager is a professional or software role responsible for collecting, organizing, analyzing, and optimizing energy-related data to improve efficiency, reduce costs, and support sustainability goals. Acting as a data manager for energy, they consolidate information from meters, sensors, utility bills, and energy management systems (EMS) to provide accurate insights into energy consumption and performance.

A Digital Marketing Manager is the professional responsible for defining, executing, and optimizing a company’s online marketing strategy. Acting as both strategist and project leader, they oversee digital channels such as search engines (SEO and paid search), social media, email marketing, content marketing, and online advertising to drive brand awareness, customer acquisition, and revenue growth. 

A Marketing Assistant is a key professional within the marketing and sales teams. This role combines operational, creative, and administrative responsibilities to support the Marketing Manager in executing marketing and communication strategies. Thanks to their versatility, marketing assistants actively contribute to project management, content creation, CRM management, and campaign monitoring.

Ecological transition consulting is a specialized form of environmental consulting that helps organizations adapt their activities to ecological and climate challenges. Conducted by ecological or sustainability consultants, often within a consulting firm, it combines scientific expertise and strategic advisory. 

An AI engineer designs, develops, and deploys artificial intelligence systems and machine learning models that power modern digital products. This role focuses on combining computer science, advanced programming, data analysis, and AI frameworks to build intelligent applications such as recommendation engines, natural language processing systems, and autonomous solutions. 

A Machine Learning (ML) or Deep Learning (DL) Engineer is an AI professional who designs, builds, and deploys intelligent systems that learn from data. Working at the intersection of data science, software engineering, and artificial intelligence, they develop predictive prototypes, algorithms, and neural networks used in real-world applications such as computer vision, natural language processing, recommendation systems, and autonomous technologies. Machine Learning Engineers typically focus on statistica examples and classical ML techniques, while Deep Learning Engineers specialize in advanced architectures like convolutional and recurrent neural networks. Their role includes data preparation, model training, performance optimization, and production deployment, ensuring extensible, reliable, and high-performing AI solutions that turn data into actionable insights.