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Why An Economist Turned Data Scientist Is Now Pursuing a PhD

Maxence Azzouz-Thuderoz is a data scientist specialising in AI and natural language processing at French consulting firm Axys Consultants. Here is how he went from studying economics and econometrics to embracing data science, and why he is now thinking of pursuing a PhD in automatic speech recognition.

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Your background is in economics. How did you end up working as a data scientist?

I went into economics and econometrics because I wanted to be an economist when I was younger. But at the end of my graduate education, I discovered data science.

I got some short lessons and, in the last year of my studies, I decided to change my plans and move to data science.

That’s how I started with economics and ended with a data scientist job.

What did the shift to data science require of you, how was the process?

It requires coding. In economics we didn’t study code that much, so I learned coding. But the transition was kind of easy because I had the mathematical and statistical background that we have in economics and specifically in econometrics, a domain of statistics and economics.

And the transition to data science and traditional machine learning tools was kind of easy because it is about the same mathematical tools, the same statistical tool. So, it was kind of easy.

What was the most challenging part then?

To work with real data. You can find open-source data that is very clean, very nice for studying something, but when you are working with real-world data it can be very complicated.

So, how did you actually manage to get from your economic studies to your current position as a data scientist?

I got a very good friend that found a job in a consulting company in northern France, and he knew that I was looking for an internship. So, he called me and said “Max, I have an opportunity for an internship as a data analyst that I think could be a nice first step for you.”

So, I started at the of commerce with a little internship for my studies and then, when I finished my studies, I went for an internship as a data analyst. Then I found a data scientist job in the consulting industry.

What’s a typical day like in your current role as a data scientist?

Our days are very different because we are always making different things, but, in general, the data scientist job is 70% working with data and 30% is about modelling. No, actually, I would say it’s 70/20 and then maybe 10% is for industrialization. That’s something we have to consider.

What’s the hardest part of working with data these days?

It’s about the information you have about the data.

Some time ago, I was working with a big French banking group, and we did not have all the documentation about the data. Documentation is a very important aspect because you can have the data, but, if you don’t know what it corresponds to, how to work with it, that is a big problem. Sometimes we were working with data that we didn’t completely understand, so we didn’t really understand what we were doing.

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It’s about the information you have about the data.

Some time ago, I was working with a big French banking group, and we did not have all the documentation about the data. Documentation is a very important aspect because you can have the data, but, if you don’t know what it corresponds to, how to work with it, that is a big problem. Sometimes we were working with data that we didn’t completely understand, so we didn’t really understand what we were doing.

Where do you see data science and AI 5 years from now?

I think we will see the first industrial applications of quantum computing in AI.

Additionally, 5G is going to change many things. We will have super-connected houses, apartments, etc. So maybe we will have new projects for AI.

Also, cell phones are becoming more powerful every year. So, I would not be surprised if we have AI technology that today we cannot make work on our phones become a reality in coming years.

For example, automatic speech recognition needs a lot of resources. It’s a big challenge. A big part of current research in automatic speech recognition is about the reduction of the parameters in models so that it all needs fewer resources. So, in the years to come, we might see some kind of automatic speech recognition technology working on smartphones, for instance.

You have decided to pursue a PhD. Why is that?

In my current job, I discovered what it was like to work on research and development projects. It was the first time I was working on such ambitious projects, and I found it very interesting. And I really became aware of the difference in understanding between someone who is simply a skilled data scientist and the scientists, PhD people.

People with a PhD were working on the same project as me, and we were absolutely not at the same level of understanding. I think this is one of the reasons why I want to go ahead with a PhD program. Because I want to reach another level.

And the other reason is that I’ve always been interested in research, the university research system. So yes, it’s a little dream, an old dream that I have and that I think could be nice to realise soon.

So, I recently started to check out universities, looking for a PhD program around automatic speech recognition. It’s a big area.


For more tips on data science, AI and automatic speech recognition, make sure to follow Maxcence on LinkedIn.


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Horizon 2050

Edna’s Garden – Chapter 1

Edna’s Garden: An 8-year-old girl with a passion for nature will turn the world upside down with her data experiments

Edna’s Garden, a story by Miquel Morales.

Discover our last story: Nadia

Edna’s Garden – Chapter 1

“Edna?” an old voice croaked from above. From beneath the pile of dead leaves she had fallen on, Edna could hear the man struggling to breathe. Wheezes and sudden bursts of dry cough formed a rhythmic pattern that spoke of one-too-many cigarette puffs while walking the dog. “For the love of Christ, Edna! Where are you? Where do you think you are going?” The man’s voice was full of urgency and rage, his British accent more noticeable than usual. Edna could not remember the last time she had seen him this mad. Maybe she simply had not.

“Edna!” In her leafy igloo, Edna could hear his steps coming down the hill as he fought his way through the dense vegetation. She held her breath. “Of all the days you could have lost your mind… It had to be today, ah? Of course it had to be today!” Just a few feet away from Edna’s face, a loose branch broke into a dozen pieces under the furious step of a muddy leather shoe. Edna held onto her precious cargo in a protective embrace. It was still warm, much like the pulsating heat that had started emanating from her ankle. She must have sprinkled it upon touching the ground. A stinging pain stabbed her leg in agreement. Great.

“I am losing my patience, little lady. Come out of wherever you are hiding. Now!” The man’s voice was now further away. It was clear that he had assumed that Edna was no longer there and was venturing deeper into the thicket. No, she would not come out! She was tired of all the stupid rules and impositions. And all because of Her. “One last time, lady! Do you want me to tell your father? Is that what you want?” No, he would not tell Dad. He never did. He loved her way too much to want her any harm. “I am going to count to three, Edna. And then, I am going to pick up my phone and call your father.” Nice try, buddy. “One…” Just a ruse. “Two… Picking up the phone, Edna!” “Peter, no!” Darn it.

Edna had just a few seconds to hide her hunting prize in one of the inner pockets of her navy blue trench coat before a hand started digging into the pile of leaves. An angry pair of tired eyes peeped through a hole in the leafy dome. There stood Peter Kahn, the family’s butler. He was soaked in sweat and covered with dirt. He was holding Edna’s Totoro backpack in one hand and a cellphone on the other.

More hurt, than angry, Edna stared back at the man with a defiant expression. “Where is it.” said the butler. “Where did you put it?” Nothing. He proceeded to unlock his phone. “I lost it while running, ok?” said Edna. “Are you happy now?” The man directed her a suspicious look. “Peter,” said Edna pointing at the swollen ankle. “I can’t walk.”


All things considered, Edna was having a great time. She was really trying to keep herself from smiling as passerby directed inquisitive and confused looks at the man dressed in dirty, eccentric butler clothes carrying in his arms a little girl with even dirtier clothes across Central Park on a Tuesday afternoon. She could have easily piggybacked her way through the park and made it a bit less awkward, but Peter was too much of a gentleman to allow that to happen.

Edna looked at the face of the sixty-year-old butler for a moment. His eyes were focused in the winding path ahead, his face as stoic as straight was his posture. He had not spoken a single word since discovering her under the leafage. Neither was Edna expecting him to do so. She knew that look very well after spending most of her life under the care of the man. He would briskly carry her all the way across the park until reaching The Pond, where he would slow down so Edna could mentally annotate the number of swimming ducks at the time and what they were doing.

It was her dad that had introduced her to nature when she was a little kid, before everything changed. She had been studying The Pond’s ecosystem for over a year now. She had built a database and tracking computer program where she carefully registered all the data in hopes that one day her research might be of use to the cool scientists at the American Museum of Natural History. Over the months, the data she collected was enough to start building a model that simulated the little natural environment she so loved. And that was only the beginning.

But this time, Peter did not slow down. Trying to get a quick glimpse of the water over the butler’s shoulders, Edna considered for a moment dropping her precious cargo where it belonged. No. It was too vital to her project’s success.

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The outlandish butler and his broken princess did not look any more fitting while crossing Grand Army Plaza. Peter even had to stop for a couple of minutes in order to explain to a concerned police officer that they were neither part of an anticapitalist street performance nor had they been involved in a limousine crash.

Edna felt sorry for Peter. The gallantry had always been there, but his new attire was simply too much. It did not use to be that way. Not until she broke into their lives and proclaimed that “elegance and taste had to be conquered one outfit at a time.” Peter, like most modern-day family butlers, used to wear what adults called “business casual” clothes.

Edna knew this from the few occasions in which she had been invited – and forced to go – to a classmate’s birthday party. She hated those kids. They were always talking about either cars or horses, summer houses and the coolest technological gadget of the season. It seemed as though their only goal in life was to copy the nearsighted lives of their parents, the superfluous, clean, organized and ultimately sombre lives of wealthy New Yorkers.

She thought for a moment of Tom Collins, that little spoiled brat. She could picture him at the school gates, leaving for home on his ridiculous hoverboard after making fun at the fact that Edna still had to be walked home by “the nanny.” She had heard those things could catch on fire. And she certainly hoped so.

Distant church bells chimed way too many times. They were pretty late. She would be furious, thought Edna with satisfaction. She had been planning this for weeks, yet another fake jewel on her crown of shiny ego.

It all started when Dad announced over dinner that he had decided to invest some money in the new restaurant of a famous art critic he recently met at a fundraising event. The guy’s name was Jeremy Talbot, and, apparently, he was as enthusiastic as Dad about saving the endangered populations of North Pacific short-tailed albatross. “So, how short is its tail compared to that of a normal albatross?” had jumped an excited Edna when her dad mentioned that fact.

But, before she could ask more about that majestic-yet-not-too-majestic-sounding bird, Bianca Salazar – Her – had come up with the brilliant idea. “That’s it, darling. We are having a dinner party!” For a moment, Edna had thought the veins on the woman’s neck would burst out of pure elation. Of course – She had been desperately waiting for such an occasion. Bianca Salazar was tall, thin and evil; her beauty extraordinary enough to make everyone else oblivious to the latter.

She had shown up at their 57th Street penthouse three years after Mom’s death. Edna was only one year old when her mother finally succumbed to the cancer. It was impossible for Edna to recall a single thing about her. She simply had this feeling, a foggy impression of having had a mother a long time ago. Somehow, she knew she came from somewhere – or rather from someone – as opposed to just having been summoned into this world by pure chance. That was definitely what it felt like with her.

Bianca Salazar had simply come along with fake smiles and pretended she had always been there. It did not work that well with Edna. She would not go as far as calling it hate at first sight – Edna was simply too young in the beginning to understand what was going on. It had been more of an awakening. By the age of four, Edna reckoned, she had had enough interactions with well-meaning human beings to recognize one without a soul when she saw it.

Dad was probably the golden standard when it came to evaluating a person’s qualities. He had taught Edna everything cool she knew or cared about, from zoology and astronomy to The Beatles and good adventure stories – The NeverEnding Story was one of her favourites.

Then there was Peter, of course. He had taught her substantially different things, the kind of things Edna wished no one cared about: how to properly eat at the table, how a lady should introduce herself to a stranger, the list of words she was not supposed to use. Well, no – That was unfair.

With her father travelling so much and the witch being, well, a witch, Peter provided Edna and her siblings with the valuable concepts of reliability and selfless generosity.

Edna looked at the butler’s face as they crossed Fifth Avenue on a red light. Peter was an honourable man. The most honourable. Edna wished they had known each other as kids. They would have been really good friends.

To be continued…

Read the next chapter of Edna’s Garden: Edna’s Garden – Chapter 2

Need tips on how to find a job in IT? Check out our IT job hunting guide.

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Growing your career: permanent & freelance IT Consultants

The ‘Holy Trinity’ of Data Science

There are probably dozens of variants of the Venn diagram that Drew Conway proposed a few years ago to capture the core skills of a data scientist. Needless to say, the role has experienced many changes since then, while rapid technological developments and the boom of AI have further propelled the profession to the top of LinkedIn’s emerging jobs ranking.

Well — we couldn’t resist putting forward our own version of the infamous Venn diagram. Like Conway’s, ours is built on three axes. However, our model focuses on broader categories rather than on specific expertise. In today’s ever-changing business world, soft and cross-cutting skills are the truly decisive factors that, in the long run, can ensure adaptability and success.  

Thus, our “holy trinity,” if you will, of data science is made up of:

  • Curiosity
  • Technical know-how
  • Collaboration

Thinking of a career in the field, or wondering if you’re doing this right? Let’s dive into each component.

The importance of a curious mind

Probably obvious, but it’s impossible to talk about science and not mention the innate curiosity that powers it. Whether you plan to explore the possibility of life in other planets or the mysteries of quantum entanglement, it is the thirst for answers to questions and riddles that will make you advance.

This, of course, applies to the problem-solving capabilities required in data science projects. Nevertheless, well-directed technical inquiries tend to fall on shaky ground whenever there are not accompanied by a good contextual understanding. Just because you’re good at playing with data and creating models that produce intricate insights and machine learning experiences, none of it is worth anything if your work isn’t helpful to the overarching goal.

For this reason, the need for curiosity expands to the domain of expertise in which you operate (i.e. finance, political studies, marketing). The more you know about the field of work of your company or department, the better questions you will ask yourself, the useful insights and models you will produce.

Note that we’re highlighting “curiosity” rather than “knowledge.” You’re going to spend many hours working with this data. Make sure it’s something that you are passionate about or at least find interesting.  

Knowing the technical ins and outs

Some describe a data scientist as someone who knows more about math and statistics than your average programmer while having greater coding capabilities than your average mathematician. Although this definition errs on side of oversimplification, it is not totally misguided.

To be successful in data science, you need to be proficient in certain data engineering and coding-related methodologies and practices. It is important not only to know how to build effective code, but also how to efficiently extract and clean data.

Additionally, there is the crucial technical knowledge that has less to do with computer engineering and more with, for instance, data privacy compliance. You must know what data sets you can manipulate and which ones you can’t, which processes can be computed on the cloud and which ones are better reserved for on-premises infrastructure. At the same time, if you work in finance or in any other field where sector-specific concepts are a basic requirement, you will have to dominate those on top of your knowledge of data science.

Playing as a team

This is where soft skills play the biggest role. Interpersonal communication and teamwork have always been one of the key factors of success Their relevance in this hyperconnected world of ours is only increasing.

There must be good cooperation between all teams and stakeholders involved in the process, and, for that, you should be able to communicate efficiently and in a compelling way. It’s not enough with working closely with developers or analysts. Knowing how to present a project in layman’s terms becomes essential if you want to be granted the staff or computational power that you’ll need to complete it.

Apart from this, you need to be well-versed in concepts like Agile development, which help teams streamline the production pipeline. Version control, a unified repository, and a good understanding between development and production are a teamwork-must in today’s IT world.