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Hi, I’m Axel, Data Scientist.
While obtaining my generalist engineering diploma at ECE Paris, I fell in love with Machine Learning and joined a research-oriented dual MSc degree specialized in Data Science taught at ” Ecole Polytechnique & Université Paris-Saclay “. I quickly became a Deep Learning aficionado, always reading about latest researches, breakthroughs, and yearning to polish my mastery of DL algorithms and frameworks.
Outside of the office, I am a huge adrenaline junkie who enjoys Motorbikes, Bouldering, Sailing, Martial Arts and basically all extreme sports, but also brain-racking activities like Chess, board games, strategy video games, etc.
What's your role at Datategy ?
My role at Datategy is twofold :
As a Data Scientist, I am expert at solving real life use cases that people (i.e prospects, clients or even coworkers) can submit. For this I need to follow a complex process starting from Data preparation and Feature engineering to obtain a data format that can then be fed to my models to train, tune and lastly deploy them.
As a ML/DL specialist, I also directly work on the development of Papai, Datategy’s AI solution. My contributions cover a wide range of functionalities that allows the platform to automate the whole process of AI projects. As Datategy’s Deep Learning wizard, I became the technical referent of every Neural Network architecture implemented in Papai, which are powerful algorithms that perform on many data types (images, complex time series, tabular data etc.).
What's does your typical workday look like ?
Every day starts with a daily meeting to discuss about current progress, goals and potential hindrance. It can be followed by a more specific talk with some coworkers to coordinate our efforts. Since softwares are build with several programming languages, every piece of code I produce must be followed by developments from my teammates to obtain an end-to-end feature on the platform !
For the rest of the day I will focus on my tasks at hand, be it implementing State-of-the-Art ML/DL models, or cutting-edge functionalities like Interpretability, Panel time series management or live monitoring. I also always take a bit of time each day to help test ongoing developments, think about new features or ways to improve existing ones. For every new idea or planned new feature, I will conceptualize its behavior and final look by drawing mockups, writing specifications etc.
Tell us a little about the Datategy AI solution
papAI platform is a collaborative and intuitive Data Science platform for managing data projects. It allows its user to build an end-to-end data flow, ranging from data processing to model deployment.
From my ML engineer point of view, the platform value resides in its capacity to offer a wide variety of powerful algorithms, easily trainable with little-to-no parametrization. All surrounding tools that are usually really cumbersome to code are also right there : data cleaning, feature engineering, interpretability, and reporting to name a few. Data Scientists will know the astonishing amount of time that can be spared thanks to this !
What's your favorite part of your job ?
As I said previously I like sharpening any skill related to ML & DL and I specifically love learning about recent research subjects like new practices, network architectures or interpretability algorithms for instance. Subjects I can dig and master in order to make papAI platform match the crazy paced evolution of AI.
What's your next challenge on Datategy ?
My current challenge is allowing better flexibility to the user in the definition of his ML operations, like the capacity to define his own metric for performance monitoring, or his own model inside the flow to make explorations. I also aim to integrate a recent Deep Learning architecture for Tabular data, featuring powerful mechanisms like Embeddings and Attention that currently make it one of the best performing algorithms on this type of data, while being interpretable.
Interested in discovering papAI ?
Our commercial team is at your disposal for any question.