After the S2DS workshop I searched for a job as a data scientist in London, which, as the cliche goes, is truly a full time job. As I am transitioning from the academic world to the private sector, I find my efforts a positive experience. The interview process helped me have a better understanding of what I actually want to do as a data scientist. Here I list my personal experiences and conclusions. — SPOILER ALERT — I was offered a data science role at SCL Elections, and commence my new job on November 24th! For more generic advice on how to prepare for a data science interview I recommend reading Jessica Kirkpatrick’s summary.
I followed a colleague’s suggestion
Commence by accepting any job interview and after gaining experience be more selective.
The logic behind this is to improve your salesmanship for when you interview for a job that you really want. Make the common mistakes early so that you are ready when it really counts. This led me to interview with companies various shapes and sizes, which, in turn, gave me a better feel for what is out there and what I should expect from projects, work environment and salary.
I realized, for example, that, at this stage, I would prefer to work in a company that understands that data analysis is a group effort. Data management, Statistics and Machine Learning are things that should be communicated with like-minded people. For example, Tesco (a major produce conglomerate in the UK and abroad) is building an excellent data science team from PhDs. Besides a competitive salary they encourage promotion of data science within the company as well as enable self development (e.g, they enable one day a month to stay at home to study). On the other extreme I met with the co-founder of BorrowMyDoggie (an application that connects dog owners and dog lovers) who was looking for a sole go-to-data-person.
The saying that “Your CV gets your foot through the door” can be paraphrased by “Your CV (and LinkedIN profile) gets you over the first hurdle of many.” Before meeting employers in person, most companies, medium to large, will first conduct various screenings: on the phone and via take-home tests.
The phone/skype calls are normally 30-60 minutes. I interviewed with: Google, Netflix, Facebook, Bookings, Tesco, SkimLinks (affiliate marketing technology), MustardSystems (sports analysis for predicting gambling results) and an NGO. These normally involve questioning on basic understanding in Statistics (e.g, Poisson, Gaussian, Binomial distributions), Coding (e.g, Tesco asked to efficiently code the Fibonacci series), Data analysis (e.g, SQL questions, and study cases). Both on the phone and in person their questions sample from the Data Scientist Venn Diagram.
Take home-exams involved either a data analysis, a coding test or both. Both MustardSystems and OpenSignal (crowd sourcing wireless coverage mapping) had me perform 90 minute coding tests via codility which is an excellent platform for testing coding and algorithm skills.
Analysis assignments varied by the company and the job details. I was mostly given one week or so to return a report. Tesco had me choose a database of my preference and use it to answer a question of my choice that would be relevant for their company. I presented results during my interview at their assessment center. Both SCL Elections and Mustard Systems gave me simulated data and had me perform an analysis involving Machine Learning predictions. WorkDigital (provide web data services) had me solve a statistics question and code a hash table algorithm in a one day notice. Perhaps the most interesting assignment was by TicTrac which involved API calls and analysis of listening trends of LastFM users (a cloud music site).
As previously mentioned, these assignments and the interviews are time consuming and filled much of my time in the two months I was searching. Overall I found them a positive experience to better realize my strong points and which aspects need improvement. In particular I felt that I need to improve two fronts: algorithms, and my online profile. Both of these accounts are worthy of their own entries, so hopefully I will write about them in the future.
This also might be worth of its own entry, but I found it super useful for my job search, so I will say a few words here: Meetups! If you live in a big city there probably are in your city many interesting meetups that you can benefit from attending. These provide the perfect environment to mingle and discuss new ideas and meet potential employers. If you’re an introvert, you might consider going a bit out of your comfort zone, for the sake of learning new cool things, or at least get some free pizza and drinks. Most meetups have volunteer lectures with some mingling, and a few are solely for networking. By attending two to three meetups a week (mostly via meetup.com) I got a few job interviews (including the company SCL Elections with which I signed), learned about Hadoop technology (and for the fun of it 3D printing techniques), learned about the-Internet-Of-Things industry, the startup scene, various languages/technologies like Python, Neo4J, R, Julia, learned about visualization technologies, got useful tips to develop a personal project of mine, volunteered in a launch party of a social media startup and even won a raffle for free tickets to Strata+Hadoop Barcelona (a Big Data conference)! In London I recommend attending: PyData (for everything python), BigData London (for updates on Data systems in London), LondonR (for updates on the R language).
Most of the above might be relevant for data science jobs world wide. Below are a few insights that are more relevant for London. I haven’t looked into all of the UK, just London.
I guess that I shouldn’t have been surprised, but the expected salaries in London are slightly lower than those in the USA. The expected salary for a junior data scientist is 30-45k£ per year for most companies, whereas within banks or large companies, one can expect over 55k£ per year. The upside, compared to the USA, is a better work to life balance. This has been one of my main considerations, and have heard this also from several Americans that I have met here. For example everyone gets three weeks of days off in the UK, which is more than the norm across the pond.
In London a lot of job hiring is through head hunters. Data science is a very broad term and tends to confuse people, especially people who’s skills are outside the Data Scientist Venn Diagram. The is results in many clueless head hunters. (One anonymous quote is tht “80% of the recruiters of data scientists are clueless.”) There are a few, however, that actually listen and try to get a grasp of what it’s all about and do a decent consistent job, but the emphasis is that they appear to be few. I can recommend recruitments firms Hydrogen (ask for Matthew), Forsythgroup (ask for Brett) and Campbell North (ask for Lewis) that were quite thorough at getting me decent job interviews. As for the clueless ones, I learned how to be patient and what to expect conversations to be like with non data/statistic people in working environments.
Besides headhunters I used online add sites like: LinkedIN, CWJobs (which has a nice feature where you can select adds by employers or recruiters).
If you find this useful, or have some experience of job searching to share, feel free to drop a line. Good luck job hunting — or better yet, avoiding too much time at it!
On the train for another meeting/interview. We’re on a road to … somewhere?