After university, it took me months and over 300 applications to land my first Data Science grad scheme.
In hindsight, there were so many things I could have done better, which I plan to share with you in this article, to increase your chances of landing a data science internship or graduate scheme.
Let’s get into to it!
👉 You can also watch the video version of this article on Youtube!
Learn Some Data Science
Before applying to Internships or graduate schemes, you should learn basic data science and machine learning.
I am not saying you need to be some whiz in reinforcement learning or an expert in stable diffusion, but knowing what data science is about will help you when applying for roles. It will also confirm that data science is a career you see yourself in for the long term.
The best way to introduce yourself to data science and Machine Learning is to follow a roadmap. I have created separate articles detailing a comprehensive study plan you can follow if you are interested, links below.
In general, you should have some knowledge in the following areas:
- Python
- SQL
- Maths and Statistics
- Supervised and Unsupervised Learning
Again, you really don’t need to be an expert in all of these things. You are applying for entry positions, so they don’t expect you to know everything nor should they. However, having a basic understanding demonstrates your interest and shows you are serious about pursuing data science, which is something employers like to see.
The final and most important thing is to make sure you do projects. If you are applying for grad schemes and internships, you probably don’t have any experience, so projects are how you showcase your skills and data science abilities.
Try to do various projects using different algorithms to solve multiple types of problems. However, one mistake I made was doing too many "shallow projects." It’s better to have 2 to 3 in-depth difficult ones than loads of easy ones. So, try to prioritise quality over quantity. This will lead to a much more exciting conversation during your interview.
Prepare Resume / CV
Another thing you should do before sending off your first application is craft your resume. I have a whole article explaining how to create a perfect data science resume that I recommend you check out, as this step is very important.
To summarise the article, the primary key points are:
- Make it one page; recruiters don’t have time to read multiple pages.
- Make it in LaTeX using one of the templates from Overleaf; I prefer the look of LaTeX to Word or Google Docs.
- Add a skills section at the top listing all your relevant technical abilities. Use terms like "proficient" or "familiar with" when rating your skills; avoid star ratings or saying you are "advanced."
- Add projects you have done and two short sentences describing what you did and the tools/tech used.
- Add in your degree, mainly if it is a STEM subject, as many data science internships and grad jobs list this as a requirement.
- Hobbies and activities are helpful to show you are not a robot and demonstrate your uniqueness.
After reviewing over hundreds of data science resumes, the above list really makes a huge difference, so make you do them!
If you want my resume template, then click the link below to check out my newsletter!
Application Process
When applying, you should be clear on what positions you would accept. Some things I considered when I was applying for grad schemes were:
- Location – I knew I wanted to be in London; anywhere else, I wouldn’t have accepted. Your preference for remote/in-office work may affect your decision as well.
- Pay – Grad schemes are paid because you are a full-time employee; however, not every internship is paid. So you need to decide if this is a deal breaker for you.
- Role – I was strictly looking for data science positions, but you may be more flexible than me and accept a data analyst, data engineer or even software engineer position.
- Requirements – I was keen on modelling, whether statistical or machine learning, so I wanted the job to involve some modelling.
- Organisation – Do you want to work for a startup or an established company. More prominent organisations tend to offer more structured internships and grad schemes, but you personally may find it more fun to work at a startup.
- Industry – Do you want to be in particular sector like finance or insurance.
Once you have your preferences set, you can start looking for internships and grad schemes with these filters.
Some people may tell you to tailor your applications for each company. While I agree with this, if you are at university or college, you probably don’t have much time to dedicate to this process. But if you do, that’s great, and consider each application uniquely.
However, I believe you should go for volume and prioritise quantity over quality. I applied for over 300 roles before getting my data science grad scheme, it was slightly excessive, but it worked for me and gave me loads of experience.
Obviously, don’t apply to every role titled "graduate/intern data scientist", but use your filters to ensure it’s a job you want to do. You can always decline the offer later as well.
Interviews
Data science interviews vary between companies, but you will likely have a coding, technical, and cultural interview.
For the coding interview, many entry-level positions often contain a simple Hacker Rank or Leetcode problem. I recommend practising a few issues using these websites. This is what I did, and I was able to pass most of these interviews, even though I am far from a great programmer.
You may also receive a take-home task or case study. With these, the results do not matter as much as explaining your thought process behind how you approached the problem. So, ensure you understand what type of problem it is and can answer why you did certain things.
For the technical and cultural interviews, I again have a whole separate article explaining how to do well in these, but the key points are:
- Study the company, particularly its cultural values and tailor your responses round them.
- Look online on places like Glassdoor or Indeed for past interview questions.
- Plan key examples and broad answers to basic questions.
- Know the projects you have done inside out, as you will probably be questioned about them.
- Be animated and charismatic throughout.
Standing Out & Unfair Advantages
The whole process above is the standard procedure for getting an internship. However, you can leverage other things to stand out and increase your chances of getting hired.
For example:
- If you have a good network or know someone in the sector, ask them if they have considered hiring a data scientist or if you can be an unpaid summer intern. This will give you real-world experience, basically what you are after.
- If you are a university student, ask to do a summer project with the university. Chances are, there will be something you can get involved with, and the worst they can say is no.
- Try to take university modules containing a computational project you can do and add to your resume.
You can also make your application stand out through:
- Write technical articles and share them online, demonstrating interest in the field.
- Do Kaggle competitions and try to do well in them; this shows your abilities to potential employers.
- Create a detailed website or portfolio to showcase your work and enthusiasm for the field. This will help establish your identity and show that you are serious about data science.
These are straightforward ideas, but many people applying for grad schemes and internships will not have these. It’s all about using the Pareto principle: Making 20% more effort gets you ahead of 80% of people.
Applying for graduate and internship positions in data science can be challenging, especially in the current job market. However, all the above points should help you land your first data science job!
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