#30 Paris Women in Machine Learning & Data Science: Ask Us Anything 🍹
We have been missing those informal moments chatting with our members at the end of our physical meetups, and have decided to organise a Zoom Apero “Ask Us Anything” for our last meetup of 2020! We were very happy and surprised by your number of questions! Here are some, grouped by category, with our answers:
1 — Transition
How to do if you want to transition from tech to business?
When you go from one field to another, your network is paramount. List the persons you know, help yourself with your linkedin contacts, and ask them questions!
Building on your current knowledge and competencies is key, you should rely one them and not trying to start from zero.
Until what age can a woman be datascientist?
Natalie Cernecka reflected on this question overnight, and came with the thoughtful linkedin post:
Coming from academia what are the biggest differences I should expect in the position in the industry? How to best approach a job search in the industry having only academic background? (type of positions: research engineer, machine learning engineer).
In machine learning & data science, academic background is valued 90% of the time! You should have a look at the requirements, and relate it to your work in academia. For example, did you deal with a package? Then you know how to code, and you know what quality entails, and so on. Also have a look at the current data science team: do people have a phd? Then you should be welcome! Best of the best: if you can contact directly someone in the team, s/he can explain in the details what the job title entails.
2 — Network
We had a couple of questions about networking — here is the one which seemed the most representative:
How to develop your network when you have a small team and you are an introvert?
You can find here a great guide of networking from Natalie! In a nutshell, networking is not about knowing very well-known people but know a couple of good people who appreciate you and your work, and can be your sponsor.
If you don’t want to go to an event and talk to strangers, you can also send a message on social media!
3 — Interviews & landing a job
How to find a phD CIFRE?
The ANRT website obviously is a good resource, and so are the labs of your school/university: the professors here are certainly in touch with companies working in their sector. You can also check the wimlds website, franceisAI, and even twitter.
We should add a warning on CIFRE PhD: when selecting a company, check for that they do sustainable efforts to support research -for real, not only because they think it’s interesting financially-.
What mistakes do people commonly make when preparing to DS/ML interviews? And at the interviews themselves?
During the technical test, don’t use technical stuff you don’t master! You might be asked to explain your choices, and this is what is key for most jobs. During other interviews, you should build a bridge between your knowledge and the company’s expectations, help them to see your value in their use case. And do prepare an introduction of yourself.
4 — Diversity
A woman was facing a sexist situation at work, and her boss was not acting or even participating. That woman was force to quit. Did she had another solution?
The answer depends on the company’s size you are working on! If you have HR, they should be the ones helping you. In startup, it’s much more difficult because of the promiscuity. You can try to find allies who support you and react to highlight that the situation is abnormal. You can also go a lawyer: it’s easy to say, and not always easy to do. If you need, you can contact one of the organizers (Caroline, Chloé, Natalie or Marie) so we can help you by a phone call or by support.
How can you attract diverse talents and show them that the company is inclusive?
BetterAllies and TheAllyance are sharing a lot of content about it. We discussed some ideas during this meeting, such as having photos of diverse people on your career pages, and training HR and managers.
Last point for this category, we would like to share with you this story from one of the attendees: About being emotional and masculine environment — just another experience. Yes, was very very emotional at least once (a lot of pressure to perform and a lot of personal things at the same time). What to do with it — I just asked for more time to do my tasks and be given a ¨room to breathe¨, not being pulled into meetings and corrections and critiques all the time.
5 — Learning
How to balance technical tools and theoretical tools?
Marie would say theoretical, because the technical will come as you are using your team tools. But it’s really up to how you want to drive your carreer, what are your strengths already and your background — there is no perfect rule.
How to start learning about machine learning? And what resources for a beginner?
Chloé-Agathe wrote a great book that you can find here!
You can also visit websites such as machinelearningmastery.com, listen to twiml podcast (they are careful about diversity), and rely on newsletters. Podcasts and newsletters are great because the content is already curated and evaluated. Don’t push yourself too much, get to the real world!
Try a few things, and find your way: there is plenty of content, it depends on how you like to learn.
Finally we had one other question which does not find the categories above: “How can you join the WiMLDS Paris Slack?” ❤️. You can send a mail to paris@wimlds.org stating your request and the mail you want to be added with, and we will do so!
If you want to view the whole meetup, here is the youtube video:
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