After spending numerous evenings and weekends learning and coding for more than a year, you finally did it! You’ve now completed your data science program, earned your shiny certificate...now what? Chances are you were looking to get a job in data when you signed up for the course. So let’s face this, it is time to get a job! The only thing that’s standing between you and success is that first data science job offer. But how?
Look no further, follow these proven steps that have helped many data science enthusiasts like you secure job offers.
Without a direction, any direction is the right direction. To travel from Toronto to Beijing, if you don’t choose the right direction first, no matter how fast and powerful your transportation vehicle is, you won’t arrive on time. Actually, if you choose the wrong direction to go, the more efficient your transportation is, the faster you get lost and fail (think about choosing rocket to go towards moon...).
In job search, the target role is your direction. Pursuing the right target role will lead to better application response rate and interview experience, given the good match between your profile and the target role’s ideal candidate profile. Thus we need to research the requirements for different roles (e.g. data scientist versus machine learning engineer) and find out the best match in terms of skills, education and experience. Have a strong CS background and interested in machine learning? Maybe machine learning engineer is a good fit. SQL is your top skills? consider data engineer or business intelligence roles. Possess strong communication skills? Data science might be a good option.
How can you make sure people listen when you speak? Say things they are interested in. In marketing speak, this is called “cater to your target audience’s needs”. When you market yourself to your potential employers, you’ll then need to have their needs in mind, so that they can “listen to you” by looking at your resume and giving you the interview. Communicating what YOU have to offer helps you stand out, among a crowd that can be at least 300 to 500 candidates strong!
How can we make sure what we say is what employers want to hear? The trick is the target role. Now that we’ve defined the target role and we have a clear understanding of the ideal candidate’s profile, we can then pick our most relevant qualifications and craft them into the personal branding message! For example, for myself, based on the insight that communication and business knowledge are valued characteristics in data scientists, I branded myself as a “business-savvy data scientist with strong statistical skills and hands-on data science project experience”.
You might think that your resume has gone through multiple revisions, why do you need to customize it again? Well, the truth is, there is no “best” in resume writing, and you can always get better! How do you measure better or not? In short, “relevance”.
You might have an incredible track record as a soccer player, but being a successful lawyer likely involves a different skillset, and therefore, your stellar sports resume might not be that impressive anymore when applying with a law firm. But, if you do have prior experience building the case to help a soccer player win the legal battle against their former club, do put it in your resume! And that, my friend, is relevance. All we need in resume tailoring is that the resume is a relevant one for the role you are applying for, in your employers’ eyes.
Furthermore, we need to incorporate our understanding of the target role (the job requirements), our branding message (which helps us stand out) into resume customization. The ultimate goal is to build a resume that resonates with employers, since we’re presenting ourselves as the perfect (yet unique) candidate they are looking for!
It is now very rare these days that people don’t look you up on the internet when they see your name for the first time. The most used tool in the professional world is of course LinkedIn. Therefore, it is not an option not to be on LinkedIn when you’re job hunting. More importantly, you need to have a strong and also, you guessed right, relevant LinkedIn profile! How is it relevant or not? Again, we’ll use your target role as the measuring stick. Want to become a data scientist? Python, R and SQL should be among your top skills. Think of being a machine learning engineer? Then we may swap in java or C++ to replace SQL and add machine learning model productionizing experience…
Other online marketing materials such as GitHub, technical blog, Kaggle profile and StackOverflow profiles are also critical in establishing your professional identify and validating your claims of qualifications on the resume, so do make sure they are also in good shape.
It’s often said that job hunting itself is a full-time job. I don’t know about you, but my personal experiences convinced me to agree 120%. That being said, without a systematic plan in place, this job offer might take forever to come!
How can we be systematic then? First, identify the activities we need in job search, and then map them out on a daily basis. Activities might include online application, networking, data science projects, algorithms and data structures practice, and interview preparation etc. You also need to give a clear daily or weekly target to these activities. This way, we’ll be able to make the most out of our limited time for job search, and when everything is done according to the plan, we’ll be constantly moving closer the final goal - job offer.
Once there is a plan in place, start actioning on it won’t be too hard, although you do need to have a winning attitude and open mindset.
First of all, it’s important to understand job search is a funnel process, meaning, for the vast majority of time, you’ll get rejections, instead of job offers. Many people fail to see this and they get discouraged and demotivated after a couple of rejection emails and failed interviews. At the end of the day, if you don’t continue job hunting, the jobs won’t come to you.
Secondly, you need to be open and flexible. Like machine learning, you job search process is also going to be improved as you learn new information, for example, a certain job search channel such as networking is more effective, at that point, you may want to adjust your search efforts and prioritize your high yield channels.
And there you have it, the six steps to get a job in data science. Whether you are a new graduate, or a career switcher, this system will help you get your dream job reliably, and faster. With this process, I was able to help many aspiring data scientists secure their job offers. Give it a try and let me know what you scored! I look forward to adding you to my success stories list!
Best of luck in your job search, in data!
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George is the founder of Datakademy where data enthusiasts acquire data science skills in weeks. As a seasoned-business-professional-turned data scientist, George possesses the unique capability to turn complicated theories into easy to understand concepts. As a passionate data science mentor, he has coached thousands of students around the world on various data science topics including data wrangling, statistics, machine learning and programming. As a data career expert boasting substantial business background and first-hand job search experience, George has helped numerous students secure job offers from iconic firms including Facebook, BMW, Amazon, Morgan Stanley, Farmers Insurance, and many more!