Repot on the Interview
Note : Here are the document formated according to the assignement
Intro
On Monday 30th of November, I had an interview with a data with one of the data scientist of the university hospital complex of Toulouse, Prosper Burq. He is my current tutor for my apprenticeship. During this interview. Inquired about his career and education. I then asked about his observations of the professional world both in the public and private sector. We then had the time to discuss the danger and the morals dilemmas that are created by the field of data science and machine learning.
Body
Carrer and studies
Prosper Burq started his cursus in math preparatory school then chose ENSEEIHT as a good ranking school for math. He initially planned to prolong his studies at Science Po to focus on political science. But during his time at ENSEEIHT he found an interest in informatics, so he specialized in software conception. After the ENSEEIHT he went on to work in the US. Upon moving back to France he worked on machine learning and data science at the Société Générale and in a laboratory of applied math of Science Po. He then had to move back into Toulouse where he got hired at the CHU to work on data science project on medical data. He hasn’t thought of any career change yet, as he doesn’t think he’ll get bored anytime soon considering the current state of the data science at the CHU. Allow he doesn’t exclude moving back to the private sector.
Work experiences
As he had worked both in the private and the public sector I asked him about if he had noticed differences and if he had preferences between the two. But to him the difference between the two sectors are minor quirks. He says that the most important factor is the scale of the groups.
I inquired about the balance of “politics” and data science he had in his work balance. He explained that his current balance is ¾ administrative and ¼ data science. But that it’s not a rule, as he is currently working on shifting a part of this administrative work to someone dedicated to it, so that he can focus on the more technical part of his job.
Data science
We often hear a lot about the dangers of machine learning, but they are often detached from the truth of the subject. So I used the opportunity to discuss the subject with someone experienced about the subject. We discussed the dangers of the miss use of the models. There are projects that could be used for discrimination and projects that could produce bias outcome when used in an unintended use case.
Conclusion
That interview was really informative on several aspects of being a data scientist. It seems that the field is decently accessible for an engineer with a good background in math and informatics. It also confirmed to me that data science is a booming field with a lot of prospects and that upon the completion of my diploma I’ll be able to find a role in that field. But that interview made me aware that the job of engineer includes a lot of managerial responsibilities. The discussion we had broadened my understanding of the social problematic of the subject of machine learning and data science. I’m now more than ever more interested into becoming a data scientist.