So you want to learn data science and get your first data science job to step up the career ladder. We don’t blame you. Data science is one of the emerging and fastest growing fields, topped up with loads of data science job opportunities and challenges. Barely a week goes by without “The War For Data Science Talent” getting a mention somewhere in the news. However, when it comes to getting a data science job, things can be tough. In the wise words of our Springboard Mentors: “The hardest part of your data science career will be securing that first data science job role.”
When you are just getting started with data science, it seems impossible to dive into the market. Employers want to hire someone with experience. A question that Springboard mentors get asked all the time from graduates and from people switching over from a different industry is: “How to get the first data science job that I need to build the experience required by most data science jobs?”
You could be a recent graduate, an aspiring data scientist from a non-IT background, you could’ve just finished a comprehensive data science training. Or you’re looking for a career change…Chances are you have heard this a lot: “ All of the data science job postings say 3+ years of experience is required, but I’ve been learning data science this whole time… how am I supposed to have years of experience?. This is something that aspiring data scientists face and is not easy to handle, particularly if you want to go through the traditional route and acquire experience. Appreciatively, there are other alternatives you can use to cope up with this lack of experience when beginning a career in data science.
“How to acquire that experience if nobody wants to hire a data scientist with no experience?”
This seems like the chicken and egg problem. One will need practical experience to get a data science job, but can’t get that experience without the first data science job. Looks like an impossible riddle right? In reality, it is not so. To relieve you from stress and save you a lot of time, we’ve filtered and listed the top strategies that you should use to secure your first data science job –
Build a Data Science Portfolio of few Standout Projects
Before you start applying, you need to learn how to market yourself as a talented data scientist, and this is where a data science portfolio helps. After you’ve completed a comprehensive data science course, you should build a few data science projects to practice using skills that you’ve learned. For this, you should find data science projects that you’re interested in building. This will give you an opportunity to shine.
Think about this: You don’t have data science job experience yet you have to show your hiring managers that you can do data science to get the data scientist job you want. But… how do you do that?
Your data science resume should have a mention about some really standout and exceptional data science projects that you have worked on. The projects do not have to be perfect but should pomp the progress you have made as a data scientist. This means if you say you know machine learning, you should have at least one machine learning project under your belt in the data science portfolio. If you don’t have work experience as a data scientist, a portfolio with at least three data science projects is critical. Portfolio projects could be work done as part of your data science courses, for a real-world client done as a freelancer, or could be a solution to an interesting real-world problem.
Get creative when choosing data science projects to work on for your portfolio. The more fun the project is, the more passionate you will sound when describing it to the interviewer. Always be prepared to discuss the strengths and weaknesses of your project with an interviewer. Preparation is the key to success in nailing your data science job interview.
No worries if your Github account is full of learning data science projects and only a few exceptional standout projects. This will give the employers a good indication of where you are in terms of your data science skill development. You should not fumble up if the interviewer pulls up your Github repository and asks you to walk through the project code. Talk about at least one tough data science challenge that you solved in your project.
If you’d like to learn more about how to build a great data science portfolio, check out our article – “How to build a data science portfolio?”
Prepare for the Data Science Interview
No player goes to a boxing match cold. One should bring their own boxing gloves to the data science interview. Warm up(prepare, prepare, and prepare) beforehand. Data science interviews are not a pop quiz, take time to prepare for them. There are different ways data science interviews are done, but the fact is that for many companies the most common type of data science interview that is here to stay is Coding (White-Boarding). If you can do well on a whiteboard then any other medium (desktop, laptop, shared documents, whatever) is a cakewalk. Practice coding in popular data science programming languages like Python or R for at least 30 -60 minutes every day. There are tons of resources online and books that help you prepare for coding interviews – Practice at LeetCode online or read Heard in Data Science Interviews book that will tell you how to prepare for your interview.
The role of a data scientist is to bridge the gap between various features of a business. Employers do not expect you to be a master of all, however, you should be able to interconnect all the features, ideas and propose solutions across domains.
Showcasing your domain expertise and individual strengths are not just enough, come across as an individual who can blend communication skills, management skills, and technical skills to get to the nub of a problem. Prepare for different categories of data science interview questions -programming, statistics and probability, machine learning, deep learning, data science puzzles, case studies.
Review our list of top data science interview questions and answers that will give you an idea on the different categories of data science interview questions.
Data science job search these days has become a lot like dating. You cannot expect to find the man of your dreams or the princess of your fairytale by sitting at home waiting for someone to call. Put yourself out there in the social environment.
A recent Jobvite survey found that 73% of employers have hired a candidate through social media and 93% of the employers review a candidates social profile before they make a final decision to hire or not to hire a candidate. Connect with like-minded professionals and data scientists on LinkedIn to increase your probability of breaking into the field. Building a network of connections allows you to ask them for an introduction and recommendations to potential contacts.
Speaking of in-person networking, did you know that most cities have frequent data science meetups where influential data scientists and aspiring data scientists come together and talk? They use this time to dive into specific real-world data science problems and how it was solved, share war stories, or talk about the novel and interesting ways where data science is used.
Drag yourself to local meetups for in-person networking. Meetups are a secret weapon for your data science career and personal branding. They give you an opportunity to meet other data science geeks and have some techie conversations with them. Find out what makes these professionals tick, what new data science tools and technologies they are interested in. Talk to them about which companies they work for and how they found their first data science job.
This does not guarantee any job leads but a handful of these meetups might really pay off in helping you find the best data science job. You never know these meetups could help you unleash some hidden data science job opportunities. Look for meetups near you at Meetup.com. Here are a few popular data science meetups in India – Bangalore Data Scientists , Delhi AI and Deep Learning , Big Data Analytics and Machine Learning , Practical Data Science
The most important step in getting your dream data science job is to find someone who already is familiar with data science careers and can provide guidance and advice as you proceed. Mentorship makes new opportunities accessible to beginners. The guidance and advice from a good mentor are what you need to get through the next set of steps to get your data science job. A mentor allows individuals to acknowledge their weak points and collectively works with them towards strengthening these skills.
Find a data science mentor that offers constructive criticism as this will help you grow and upskill along your data science career path. Nurture your data science skills with the help of professional data science mentors by working with them on industry-level data science projects. If you are faced with a time crunch or are having difficulty finding a good data science mentor, you always opt for mentored data science programs that can enhance your practical data science skills, adaptability, motivation, self-worth, and of course, salary.
Time is Up…You Can Do It
Thank you to all of you who read it this far! We hope you found this article helpful in your next data science job search. Our last piece of advice is: do not worry about just getting a data science job. There is a huge demand for data science skills, even outside the major tech hubs. Focus on getting the right data science job – a data science job role that involves working with data science tools and technologies that you love, at an organization that treats its employees well. It is not at all times possible to get both at your first data science job, however, once you get your foot into the door, pursuing a desirable and lucrative next data science job will get a lot easier. Well, that’s it. We know it sounds easier when it’s written. However, if you are passionate and determined to get a data science job, you can make it happen. Good luck as you travel along your data science career path. Hope it takes you somewhere even better than what you had planned.