How I Got 3 Data Science Job Offers in One Month
Do this to 10X your chances of landing a data science job.
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I am a data scientist.
When ChatGPT was first released in 2022, people told me that AI was going to take my job.
Now, two years later, not only am I still employed, but I also have received multiple job offers from reputable companies.
In fact, this year, I received 3 data science job offers in a single month.
All these roles offered flexible working hours and competitive salaries, and interviewing at these companies made me realize something:
Data science roles are not going anywhere.
If you are a beginner in the industry or aren’t sure whether data science is still worth it in 2024, this article is for you. Here’s what you’ll learn after reading this article:
- How the role of a data scientist has changed over time
- How to prepare for data science interviews
- What employers are looking for in data science candidates (and how you can stand out among the competition).
Data Science Has Changed
AI has removed a lot of the heavy lifting from data science roles.
As a result, coding tasks that once took an entire day can be completed in a few hours.
Employers are aware of this, which is why the data science hiring process is now focused on your ability to:
- Solve case studies with data
- Communicate with non-technical stakeholders
- Help executives make sound, data-driven decisions
You see, although AI models are great at generating SQL and Python code, they still cannot explain the concept of statistical significance to a non-technical stakeholder.
They can’t brainstorm, evaluate multiple product decisions, and explain to the Head of Product why you should launch X instead of Y.
Human intervention will always be required in this process.
Companies need to hire individuals who have the following skills:
- Technical skills (Python, SQL, data manipulation)
- Knowledge of statistics
- A deep understanding of their business model and product
- Strong communication skills
A data scientist who has the above skills can oversee the output of AI models and use them to make sound business decisions.
If there’s one thing you take away from this article, let it be this:
Companies are no longer hiring data scientists who solely have strong technical skills.
While you must know how to code, query databases, and build dashboards, interviewers are now more focused on domain expertise and problem-solving skills.
How to Prepare for Data Science Interviews
First, brush up on core skill sets like SQL, Python, ML, and statistics. I suggest using platforms like HackerRank and Leetcode to practice your programming skills.
StatQuest and StatQuest YouTube channel are also great resources to prepare for the technical interview.
In my experience, the technical interview was the easiest stage to pass because most companies ask standard questions that will be covered in the resources listed above.
The case-study interviews, however, are more difficult.
For example, you might be given a dataset with a business problem like this one:
“Given a dataset with historical sales figures, what steps would you take to predict sales for the next 3 years?”
You need to explain your thought process to the interviewer and work with them to solve the problem.
In some cases, you’ll have to demonstrate your approach to solving this problem in Excel or Python.
If you have no prior job experience, there are two ways to prepare for business scenarios like the one above:
Projects
You can simply go to ChatGPT and ask it to give you a business problem statement and dataset.
You can even specify the difficulty level of the project and have the chatbot score you based on how well you execute it.
Volunteer work
If you have the time, I recommend looking into organizations like DataKind.
NGOs often hire volunteer data scientists, and the barrier of entry is lower than that of a paid job.
You can work on a real company’s data and gain experience — which will help you ace your data science case-study interview.
How to Get a Data Science Job
Here’s a step-by-step guide on how to get a data science job. This is what I used to land 3 data science offers in one month:
- Coding: Learn and practice SQL and Python.
- ML and Statistics: Practice answering key machine learning and statistics interview questions.
- Projects: Get ChatGPT to give you a dataset and problem statement, and create data science projects to solve the problem.
- Communication: After creating a project, practice communicating your insights to a non-technical stakeholder. This free course on data storytelling is a great place to start.
Once you’re done with interview preparation, you can start the job application process.
I recommend reaching out to recruiters and hiring managers directly once you apply for a position — personally, establishing a relationship with the recruiter is what helped me land all 3 job offers.
If you have time, I also recommend reading about the company you’re applying to and personalizing your emails to recruiters.
Although this is extremely time-consuming, it shows that you’re willing to go the extra mile.
Also, resumes do get lost in the pile. Sometimes, you might get auto-rejected because of an unreliable ATS tracking system.
Connecting with recruiters will set you apart from other candidates and dramatically improve your visibility.
Final Thoughts
There is a lot of uncertainty in the tech space right now, and massive changes and organizational restructuring are taking place due to AI.While this can be overwhelming, it doesn’t mean that data jobs will disappear overnight. It simply means that you’ve got to acquire the knowledge necessary to adapt to the transition.
Natassha Selvaraj is a self-taught data scientist with a passion for writing. Natassha writes on everything data science-related, a true master of all data topics. You can connect with her on LinkedIn or check out her YouTube channel.