From Graduate to Data Science: How to get a job that doesn’t yet exist
This is how I’m becoming a Data Scientist without experience in the traditional sense. The market for Data Scientists is screaming out for 2+ years of experience but is correctly sceptical of persons without it. To solve this problem I need to take a different route to the role. I’ve been talking with a number of Data Scientists and Tech Recruiters and taken onboard all that they’ve said. Through the huge amount of information that I’ve been given I’ve been able to come up with a plan which I am in the middle of testing. All of the evidence says that my plan is working.
In Data Science terms, I’ve produced a model from a large amount of data and the data on which I am now testing it currently fits the model.
The base that I’ve built
For this plan to apply to anyone else I need to explain the position I was in before I started to try to become a Data Scientist. Also, should you not want to become a Data Scientist the overall plan should be adaptable to any recruitment market where the market isn’t in your favour.
I’ve interned at multiple Tech startups. The experience from these internships allowed me to learn about myself and learn in what role I would be best utilised. They also enabled me to build an initial network which has made my quest a lot easier. I also have an MSci in Physics; a quantitative degree gives me a great standing and when job hunting an appropriate degree will always help. Those of you without a degree, some of the most talented people I know don’t have a degree. They work hard to make themselves stand out and they’re doing well, these things are possible.
That covers experience and hard skills. But soft skills — people skills, team skills and other types of skills are a different beast. I believe that this plan should work irrespective of soft skills but good people skills will speed up the process and make you stand out. If these skills are weak they should be worked on. How? Reading novels is a great start, Stoner is a personal recommendation.
The Plan
Step 1 — Network, Listen and Learn
How can you know you want to do something without experience or knowledge? You’ll be asked that question first. Experience can come through networking and practice (Step 3) but knowledge is the first priority. Knowledge can be found online (Step 2) but useful knowledge will come from people.
There are 2 types types of people you want to find and learn from. Data Scientists and Recruiters. Ask people in your network to introduce you to them. Ask to talk to them and learn from them. Ask how they became Data Scientists and how you can become one too. Ask recruiters how to stand out, not just in a pool of Data Science CV’s but in the whole recruitment market. Listen and Learn, that’s the most important part. Ask them to introduce you to further people. If you’re truly interested and you can put that across in your conversations they will want do that.
If you can’t find these people through your network there are other ways. This is the reason you’re on LinkedIn. Search for Data Scientists near where you are based and send them a message. Some will ignore you, many won’t. The key is to do as much as you can of the heavy lifting yourself.
Meetup.com was designed for people such as yourself. In London there are a number of Data Science meet-ups (PyData, Data Science Festival, and many more) which I attend looking to learn. At these events I have met some great people and I learnt a lot.
Then there’s the secret sauce. Apps and services that connect individuals. Take Tinder, remove the hedonism, make it professional. You’ll end up with Shapr. But Shapr is more noise than signal (much like Tinder). The real secret sauce is JamieApp (only currently for Tech and Finance). That’s how I’ve met movers and shakers.
Step 2 — Learn
Fill in the gaps of your knowledge. Begin to develop the skills needed for the job. For Data Science this means coding, statistics and communication. You can learn the hard skills online easily and cheaply, no need for another stint at university. Udemy (when on sale) is unbelievable value for money. Udacity’s Nanodegrees are great. DataCamp is an excellent place to start. Alas, there are too many options and that makes it difficult to start, I was only able to come to a decision through someone else’s hard work.
Learn how Data Scientists communicate. How they articulate what they learn to people who know the inside knowledge and those who don’t. You can do this by signing up for appropriate newsletters (Data Science Weekly) and searching for appropriate articles on places like Medium, Google, Reddit and LinkedIn, KDNuggets. Learn to visualise well. Excel graphs don’t cut it anymore. Tableau and Carto have public versions enabling people to create great visualisations. This talk is also useful.
Listen to Podcasts. This is how Data Scientists continue to learn and solidify their knowledge by hearing different viewpoints. Some great ones are Data Skeptic, More or Less and Partially Derivative.
Step 3 — Practice
You can learn to code. You can learn the Data Science process. You can have a PhD in Statistics. But without experience you’ll struggle on the job and need guidance, you may not have that. How to get experience outside of a job? Kaggle, recently acquired by Google, is a place where Data Scientists congregate, practice and learn from each other. With datasets and their associated challenges you’ll learn to be a data scientist beyond what you can learn from the online courses in Step 2. A similar place is HackerRank. Anything that you produce post on GitHub and put your GitHub on your CV. Learn the Git process, how it works and how it will apply to you. Don’t worry if your code is bad, the more you practice the better you will become.
Step 4 — Apply
I have written previously about why Junior Data Science jobs don’t yet exist. In fact, they’re starting to appear out of the woodwork. But that doesn’t mean they’ll be as high quality as a structured Grad Scheme. The world’s lack of experience with Data Scientists will mean that, until they are given a good direction, their skills can be and will be easily wasted.
But when that right role comes along you’ll want to be ready. And being ready comes through practice. You can practice the role in Step 3. But if you’re not ready for interviews Step 3 will have been a waste of time. Talk to the Data Science recruiter that you would have found in Step 1. Ask them if you can practice with them. If they agree, they’ll help you be ready and you’ll help them too by helping them practice and learn the right questions to ask.
Some Motivation
This isn’t the only way. This is a plan that I’ve put together from the advice I’ve received. It’s a difficult, time consuming plan that could probably be optimised. In a mature market a graduate would be likely earn their stripes cleaning data. But without experienced Data Scientists and Data Engineers creating useable, well structured data there is no need for the typical graduate scheme placing graduates in pure Data Science roles, yet.
Besides, if you do this, you’re a pioneer. School and University were guided, there was a system in place designed by others to get you from A to B. The path was well trodden and optimised. Going from University to Data Science is a path that is yet to be trodden. I have learnt, and am continuing to learn, a lot about myself during this journey and I hope that if you were to go on a similar journey the same would happen to you. Through this plan I think that I’ll be ready and raring to go when I get to the first rung of this career ladder.
