Life at Datategy : Miloud Damene

Hello everyone, my name is Miloud DAMENE. 

I graduated from the higher National School of Computer Science (ESI-Algiers) with a dual degree: an engineering degree in information systems and a master’s in computer science. Later, I pursued a second master’s degree in “Digital Transformation for Industry” at Université Paris-Saclay. My academic journey was primarily focused on data analysis and artificial intelligence. 

Since 2021, I have been working at Datategy as a data science engineer, actively contributing to the development of our platform papAI. Our mission is to make AI accessible to everyone through an easy-to-use no-code platform, enabling the creation of any AI project from start to finish with minimal coding. The platform emphasizes clear workflows, high-performance results, and explainability.

Life at Datategy : Miloud Damene

What is your Role at Datategy?

As a data scientist and one of the main developers of papAI, my main role is to stay up-to-date with the latest advancements in AI and machine learning to explore how they can be efficiently integrated into our platform. I contributed to the implementation of all existing machine learning use cases on papAI, with a particular focus on the time series forecasting use case. This has evolved into a robust and comprehensive module, featuring a wide range of models and an advanced explainability component. 

The goal, in addition to providing an enhanced user experience, is to ensure clean and well-structured code that is easy for other developers to understand and maintain. This includes thoroughly testing the code with comprehensive unit and integration tests to guarantee its reliability and functionality. 

Since our platform extends beyond just the machine learning component, I have frequently contributed to other areas as well. This includes data cleaning, where I worked on integrating new cleaning operations using Spark, developing new visualizations, and implementing various data manipulation features to enhance the platform’s overall functionality.

What does your Typical Workday Look Like?

My workflow is organized on a weekly basis, with a predefined list of tasks to accomplish each week. We use Jira to create and track the progress of these tasks. My workday begins by checking my Jira dashboard to identify any high-priority tickets that need immediate attention before proceeding with my regular tasks. This helps me plan my day effectively. 

Once I’ve outlined my tasks for the day, I communicate with the team, sharing what I plan to work on and what I completed the previous day. After that, I dive into coding. For new features that require design and specification, I collaborate with relevant stakeholders—front-end and back-end developers, as well as designers—to shape the feature. On such days, a significant portion of my time might be dedicated to brainstorming sessions and discussions about the new feature. 

Collaboration within the data science team is also a key part of my role at Datategy. We hold bi-weekly meetings to discuss topics of interest or new ideas that could benefit the team and our projects. 

In general, my typical workday runs from 9:30 AM to 6:30 PM, with a lunch break from 12:30 PM to 2:00 PM. This structure allows me to maintain focus and stay aligned with team goals while ensuring a balanced approach to my tasks

What's your Favorite Part of your Job?

My favorite part of my job is the constant opportunity to learn and innovate. I enjoy staying up-to-date with the latest advancements in AI and machine learning and finding efficient ways to integrate these innovations into papAI. It’s incredibly rewarding to see how these improvements enhance the platform’s capabilities and make AI more accessible to our users.

I also appreciate the collaborative aspect of my work. Whether it’s brainstorming new feature designs with front-end and back-end developers or exchanging ideas with my fellow data scientists during bi-weekly meetings, these interactions push me to think creatively and refine my skills.

Lastly, I find great satisfaction in contributing to a clean, well-structured codebase and ensuring robust testing. Knowing that my work not only improves the user experience but also sets a strong foundation for other developers is a source of pride for me.

What's your Next Challenge at Datategy?

My next challenge is to further elevate papAI capabilities by integrating more advanced tools that optimize AI and machine learning in the most efficient and scalable manner. This involves exploring and implementing state-of-the-art techniques, refining and optimizing our existing modules, and expanding key features, such as explainability, which plays a crucial role in ensuring transparency and trust in our models.

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Life at Datategy : Miloud Damene
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