How to Land a Data Science Job in 2024

Based on the number of job postings, demand for data scientists in the UK jumped by 231% between 2013 and 2018. Even with the recent tech layoffs, demand for data scientists will continue to grow in 2024 and beyond. Indeed UK alone lists ~1,300 data science jobs as of this writing. And with Glassdoor reporting an average data science salary of £61,871 per year and a maximum salary of £111,000 per year, now is a great time to launch your data science career.

While recruiters see a massive number of applicants, employers still have trouble finding enough talented professionals. And getting into the data science field can feel even more daunting as a soon-to-be or recent graduate. But by following the guidelines below, you can show employers you’re qualified for their open data science positions.

Before you apply (and throughout the job application process)

Data science employers value an applicant’s experience more than formal education. Keep in mind the following tips to make your application more attractive to employers.

Gain data science practice

Earning a degree or completing a boot camp is often the first step to becoming a data scientist, but taking these steps alone may not be enough to land your desired data science job. To make yourself a more competitive candidate, gain more data science experience by:

• Participating in competitions (e.g., Kaggle, Driven Data)
• Volunteering on data science projects (e.g., DataKind, Data Science for Social Good)
• Doing freelance work
• Working on personal projects

Make a portfolio

In addition to making your code available on open-source repositories, increase your visibility by creating a personal project website. 

Write about your work as simply as possible so recruiters don’t get lost in jargon. Detail both what you did and why. For roles beyond data cleaning and data engineering, show your critical thinking skills by summarising and interpreting your results. 

If you don’t have or want to make a designated website, ensure you have thorough and organised documentation (readme) on your GitHub or in your Jupyter or Kaggle notebooks.

Network online and in-person

As in any profession, it’s easier to get a job in data science through personal connections rather than via blindly sending out applications. So use sites like LinkedIn or your university’s alumni page to access your existing network and make new connections. Also, attend data science conferences to build your skills and meet new people. 

Your connections can point you towards open jobs and, if they work at the companies you’re applying to, help you better understand your target roles and what you’ll need to build a competitive application. 

Applying for data science jobs

It’s not uncommon for applicants to send out hundreds of applications before landing a job, but don’t sacrifice quality for quantity when preparing your application materials. Follow the below tips to increase your chances of landing your desired data science job:

Customise your cover letter and CV to the job description

Tailor your cover letter by addressing the employer by name. And rather than reiterating the qualifications you listed on your CV, state what you can bring to the company. Use unique achievements — such as winning a Kaggle competition — to show recruiters that you can help solve problems that will help the company achieve its goals.

Additionally, ensure your CV’s work experience and skills sections address the job description. 

For example, a job posting might read, 

‘Collaborate with data scientists to mine enormous amounts of statistical data for insights.’

This job description calls for candidates with good communication skills, which you can demonstrate by stating the size of the teams you’ve worked on and the results you achieved.

You might respond by writing, 

‘Effortlessly worked with 8 data scientists to understand water usage in southeast England, leading to data-driven recommendations to reduce water use by 30%.’ 

The above job description is also trying to ascertain whether you can work with large amounts of data. In response, you can state how big the data sets you’ve worked on in the past have been. 

Also, the employer wants to ensure you have data mining and statistical analysis skills. To show that you meet these requirements, you can list your relevant abilities in the skills section of your CV and provide specifics in your work experience bullets. For example, you might say,

‘Performed exploratory data analysis, regression, and data grouping using SAS to build a model of user age and app usage.’ 

Lastly, build a CV that’s designed to pass applicant tracking systems (ATS), the software most companies use to weed out unqualified applicants. Use keywords from the job description and avoid fonts or graphics the ATS won’t be able to read to ensure your application gets seen by recruiters. 

Be prepared for any kind of interview

Compared to interviews in fields like software engineering that follow a standardised format, interviews in data science can vary widely. The best way to prepare is to seek advice from people who’ve been through the interview process at the company you’re applying to. But if that’s not available, be prepared for interviews that may include one or more of the following tasks:

• Coding tests or longer coding projects
• Statistical data analysis
• Verbal interviews to test your knowledge of the company’s products or services

What if you still can’t land a data scientist job?

Even the most streamlined job searches take time. So increase your chances of landing a position by constantly working on gaining new abilities and improving existing skills. Additionally, pursue freelancing opportunities to build skills and work experience on your CV. But, if your job search is extending into months and years with no offers, you may need to alter your approach.

For instance, you may need to switch the type of companies you’re applying to. Start-ups and smaller companies are more likely to accept you than larger companies that have more applicants and can afford to be extremely selective.

Accepting any role related to data science, especially if you’re fresh out of training, can also lead to a future job in data science. Depending on the company, it may be easier to move to a data science role from a lower or different position, such as a product analyst or data engineer.

Additionally, you can consider getting an advanced degree. Especially if you’re determined to work for a big tech company and enter at a high level, it may be helpful to pursue a Master’s degree or, in rare cases, a PhD. However, given the time and money, an advanced degree will require, seek advice from those currently holding the job you want before returning to school.

Featured image by: Christina Morillo