We recently caught up with Giulio Palombo, Founder of Data Science Europe. We’ll be discussing his experience working as a Data Scientist in Silicon Valley and his motivations for moving into Data Science education. We’ll also be shedding some light on the Data Science market in Europe and how he works with his students to ensure they all finish with positive outcomes.
Hi Giulio, Thank you so much for taking the time for this interview. We’ll start with a few questions about your backgroumd
Q : Can you tell us a little bit about your background and role at Data Science Europe?
A : I worked as a Data Scientist for a few tech start-ups in Silicon Valley. Most recently, I was a Data Scientist at Airbnb for two years. In my previous life, I was doing research in machine learning applied to Particle Physics at Caltech.
At DSE, I am in charge of recruiting students, I do part of the teaching and I schedule job interviews for the students. There are other data scientists coming from US and Europe who also help me with the teaching part as well as set up interviews at their companies if the students are interested in working there.
Never. And I would never do it. I chose those guys and from the moment I chose them, it is my responsibility to get them the best possible job. If things are not working out during the course, it is my fault and therefore, my responsibility to fix it – Giulio Palombo
Q : How did you get into Data Science Education / Training?
A : Since my first company – JustAnswer – I mentored younger data scientists and I found it very gratifying. I feel teaching and empowering people to reach their true potential is the best possible job in the world. So I quit Airbnb and decided to do it for a living.
Q : What is your definition of a Data Scientist?
A : Someone who can improve the product using data. When a Data Scientist from Twitter talked to our fellows, she said that “the hardest part of a data science job is to translate a business or product problem into a math problem and then be able to solve the math problem to improve the business”. To me, this is the best definition of a Data Scientist I’ve heard.
While there are obviously differences between data scientists who work on online models (writing production code) or offline models, in both cases the goal is to use data science to improve the business.
Who is not a data scientist: someone who just writes SQL queries for a product manager. That’s a product analyst. There is no science there and no thinking. I think more than 50% of current data science jobs are just rebranded product analysts,
Thanks for the introduction. Now lets talk about your motivations for starting a data science bootcamp, what you look for and how do you select new students.
Q : What’s the 1 minute bio / introduction on Data Science Europe?
A : We help people become data scientists at their dream companies. They tell us the companies they want to work for and we set up interviews there at the end of the course.
During the course, we have real data from a Silicon Valley company and we spend 6 weeks recreating a standard data science project in a tech company: pulling data, cleaning data and building models. At each step, we teach the tools and the theory required to be successful.
Q : How did the idea for Data Science Europe come about and what do you hope to achieve with it?
A : There are tons of students in Europe with tremendous analytical foundations and great potential who end up doing crappy jobs. The goal of this course is to let them know exactly what the opportunities in front of them are and to make sure they are prepared so they can take advantage of those opportunities.
Eventually, I want to have built an outstanding network among the best Data Scientists in Europe so that these guys can help each other in their careers (starting companies, becoming executives, etc.)
Q : How do you screen and select students / fellows for your program?
A : Candidates apply via the site. If selected, we have a 1 hr Skype conversation. Candidates are also encouraged to talk to previous students and if they do that, their chances of admissions goes up.
Q : Can you describe the typical background (academic / professional) and the type of skills you look for in your students / fellows ?
A : A quantitative degree (Physics/math/stats/etc.). An advanced degree is preferred but not required. We had fellows with just a bachelors become data scientists at great companies. Knowledge of at least one programming language (any language is fine) and some examples of interest in data science (i.e. we don’t want people who try to become Data Scientist because it is easy to get a job and salaries are high).
The most important things I look for are examples of analytical minds, a passion for data and cultural fit, where cultural fit means willingness to help others. A big part of what we are trying to do is to build a network among the best data scientists in Europe and in order to do that fellows have to be willing to help each other, because that’s the only way to build a network.
No, I don’t have a hiring day. Students choose where to interview. A hiring day is not in line with the culture of what we are trying to do since it implies getting money from companies and pushing the students to work there regardless of how good or bad that job is. – Giulio Palombo
Now lets switch gears to placement, since the reason most students go to a bootcamp is to transition to a Data Science job
Q : How many cohorts have you gone through?
A : Finished the second cohort about two weeks ago
Q : What is your typical cohort size?
A : Less than 8
Q : Can you share what your placement numbers look like for your most recent cohort (within 3 months of graduation)?
A : Cohort 1 : 100%
Cohort 2 : Finished two weeks ago, still interviewing
Q : Can you also share your historical placement rate?
A : 100%
Q : What percentage of your fellows eventually get Data Scientist vs Data Analyst vs Other technical jobs?
A : 100% data scientists
Q : Can you share with us where some of your graduates work or will work?
A : King (UK), RyanAir (IE), Catapult Sports (UK), Accenture Data Science LAB (ES), IgnitionOne (US)
Q : Can you share with us what industries your graduates work in?
A : Technology companies.
Q : Are students ever asked to leave or are kicked out mid-way through the course or at anytime during the course?
A : Never. And I would never do it. I chose those guys and from the moment I chose them, it is my responsibility to get them the best possible job. If things are not working out during the course, it is my fault and therefore, my responsibility to fix it.
Also, students can leave any time and don’t have to pay anything if they leave within 2 weeks or they have to pay a small fee if they leave after the first two weeks. So far, no one left.
Q : Do you have a hiring day and what percent of students are placed from a company they meet at hiring day?
A : No, I don’t. Students choose where to interview. A hiring day is not in line with the culture of what we are trying to do since it implies getting money from companies and pushing the students to work there regardless of how good or bad that job is.
We do have data scientists from companies come and talk about their work. If any student is interested, guest data scientists will then get them an interview. I find this model to work well because it gives us time to all talk and discuss together about each company after the guest data scientists leave. Guest data scientists are chosen based on where students would like to work.
Q : How do you prepare your fellows to be very competitive for Data Science jobs?
A : They have to understand the theory behind machine learning. eg: Everyone can press a button to build a random forest. My fellows know what a random forest is, when it works well, when it doesn’t, how to optimize it and why, etc. My fellows are not people who press buttons, they understand what they are doing.
Q : For organizations looking to hire Data Scientists what should they look for..Ivy Degrees, PhDs, Extensive experience, Quantitative Background, grit and determination?
A : Ability to think. Take a past project the candidates has worked on, go through it and ask step by step why they chose a certain approach. Many candidates don’t have an answer for that! Also, ask what-if questions.
These kinds of questions will give an idea of how the candidate thinks and works as well as will typically lead to some theoretical discussions that will help evaluate how solid the foundations are.
We don’t take money from companies. We work for the students, not for the companies. If we think a company sucks, we tell the students that company sucks (we do it a lot!). We help set up interviews wherever the students want to work. By not accepting money from companies, students can trust that we give them the best advice (and companies have offered us money for referral fees, but we said no to stay independent!) – Giulio Palombo
Thank you for sharing those numbers. Now we would like to get a feel for general administration at Data Science Europe and how you improve your process
Q : Can you give a short summary of a typical day in the life / week in the life for your fellows?
A : This really depends. We spend all day and evening working together on data science problems but the kinds of problems we work on change day by day, so it is really hard to give an answer to this question. Also,, on some days there are mentors who come in and meet with the students. Some other days we visit companies, etc.
Q : How do you improve your process and instruction from cohort to cohort at Data Science Europe ?
A : We have a mailing list for all students from a given batch. 2 months after the end of the course, all students send an email where they explain what they didn’t like about the course. It is totally transparent as everyone sees what others say. We then analyze the pain points and in the following batch I try to solve the issues raised by the students.
Q : What skills and tools do you think should be emphasized more in Data Science education and what skills do you think will be most important for Data Scientists in the next few years?
A : Business and product sense. Data Science is a tool to improve the business. I saw amazing machine learning scientists working in the industry and being totally useless because they couldn’t think from a product and business perspective. A great Data Scientist always has the product in mind while doing their job.
Also, Data Science education should teach everything that people typically learn while working: how to network, what’s the point of networking, how to convince your boss about your idea, etc. These things play a huge role in an employees’ success and no one teaches them.
Q : The Data Science bootcamp space is getting quite crowded, how does Data Science Europe differentiate itself?
A : Transparency: all students and their contact details are on the site. Candidates are encouraged to just reach out to them.
Word of mouth: Students will talk about their experience and therefore more students will want to join.
We don’t take money from companies. We work for the students, not for the companies. If we think a company sucks, we tell the students that company sucks (we do it a lot!). We help set up interviews wherever the students want to work. By not accepting money from companies, students can trust that we give them the best advice (and companies have offered us money for referral fees, but we said no to stay independent!)
Q : How do you help students deal with burn-out?
A : I use the personal project to make them rest a bit and have some fun. The first weeks are about coding in Hive and R, and that’s not too hard. When we get to Machine Learning, things get more mentally tiring. So, towards the end of the course, we do machine learning in the morning (theory and exercises) and in the afternoon they can mentally rest while playing with their own personal project.
Q : How do you work with your students to ensure they’re assimilating most of the material in a very short period of time?
A : A couple of ways. Firstly, we have a shared folder where I see everyones code. That way I can get a sense for each guy and whether the topics have been understood or not. If not, I individually spend more time on that (this was an idea given by a student and it worked great. Also, that way everyone can see everyone elses’ code which helps a lot!).
Secondly, we all live together during the course. In the evenings, if they want, I am available to talk and make sure things have been understood or go through some concepts again.
Great, now we’ll be asking a few more questions for finish out the interview
Q : What do you feel is broken with Data Science education in general and how is Data Science Europe trying to fix it ?
A : Huge disconnect between industry and academics. At least in Europe, education is too theoretical. Also, students have no idea how to apply for jobs, reach out to companies, figure out when a job sucks and what to expect in a job interview.
Q : What problems in Data Science keep you up at night?
A : In my course: Everything worked great for 5-10 people per batch. We became friends, everyone got a great job, etc. Is the entire thing somehow scalable ? Can I help more than those few people reach their potential?
Q : Have you faced any major challenges in running the Data Science Europe ?
A : Not really.
Q : What markets / verticals are you currently focused on ?
A : Technology
Q : How do you feel the European job market differs from the US market?
A : Tons of fake data scientist jobs in Europe. It is harder to find a Data Science job which is not just writing SQL queries in Europe, but things seem to be improving recently. Salaries are lower in Europe (except for Switzerland).
Europeans historically care more about experience than Americans. Never heard in the US something like: “we want people with 3+ yrs of experience”, while it is pretty common in Europe.
Q : Who are the Data Scientists that inspire you?
A : I was lucky enough to get in touch with some of the best data scientists in the world during my career. Chris Gutierrez and Luke Dziurzynski in the US and John McAuley in Europe are the people I tried to learn the most from.
Q : Any parting words for prospective Data Science students or Data Scientists that are just starting their careers?
A : The first job is really important. If the first job is bad, people lose self-esteem and this can impact their entire career. Don’t settle in the first job and remember a data scientists’ worth to a company is about 10X her salary. Europeans often don’t know their true value (unlike Americans who tend to know exactly what they are worth). Make sure you know what you deserve and ask for it.
Thanks again Giulio for the sharing some of your thoughts and insights with us. We really appreciate it .
To find out more about Data Science Europe do reach out to Giulio Palombo, engage with Data Science Europe on twitter @DataScienceEU or reach out to his former students. Data Science Europe is also currently accepting applications for the next cohort starting in Jan / Feb 2016.
Please stay tuned for the other Data Science Bootcamps Founder Interviews we have in the pipeline at Data Science Bootcamp Founders Interview Series