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How to Apply for Data Science Jobs: The Trick to Landing the Job

August 30, 2021
by
SafeGraph

Data science is a broad discipline with a range of applications and unique roles. When applying to jobs, it’s important to do proper research about what the role entails and the industry you’ll be working in. It’s also important to consider what employers are looking for in a data scientist. These can have a huge impact on what you do each day, and your overall satisfaction — and success — in a role.

To help you apply for data science jobs, we’ll cover the following:

  • Do you need a degree to be a data scientist?
  • How to find a data science job
  • How to apply for data science jobs: 7 tips

Before we get into tips for the application process, we’ll briefly discuss whether or not you need a degree and where to look for data science jobs.

Do you need a degree to be a data scientist?

No, a degree is not required to be a data scientist. However, most data scientists have post-secondary education in related fields. In fact, many data scientists have advanced degrees, such as a masters or PhD in their field. Data scientists rely on their hard, technical skills most to get a job in their field.

Bachelor or advanced degrees in the following fields are typically a good foundation for data science roles:

  • Statistics
  • Engineering
  • Math
  • Computer Science
  • Physics

When applying for roles as a data scientist, you will rely mostly on technical skills, such as your knowledge of programming languages, data analysis solutions, statistical methods, and how to implement this information for practical results.

How to find a data science job

When applying for data science jobs, you first need to know where to look. Below, we cover the top places to look for data science jobs.

1. Browse online job boards

Job listing sites are some of the first resources you think of for finding a job in your field and the industry that you want. However, since these are the first places most people go, they are very popular, competition is high, and the boards can often be saturated with listings of mixed quality.

There are also a limited number of opportunities on these online job boards, as you’re only able to apply for what people list here. There will be many opportunities that are never listed on these job boards that you will miss out on entirely.

That being said, there are a number of opportunities and if you can sift through the listings, you can find good quality opportunities.

Check out some examples:

  • Indeed: As one of the leading job board sites, Indeed is a great place to find data science jobs. You can even apply directly within Indeed to make it extremely simple.
  • Glassdoor: You can find a wide range of job postings on Glassdoor, easily filtered by industry, role, and more.

There are also a number of job boards specific to the data science industry.  As a specific marketplace for these industries, you’ll eliminate a lot of clutter you’d find on more generic sites.

  • KDnuggets: As a reliable data science hub on the internet, KDnuggets is a great resource for finding data science job listings.
  • Outer Join: Outer Join is a job board focused on remote jobs in data science. If you’re looking for data science jobs you can do from home, this is a great place to look.
  • StatsJobs: StatsJobs is a job board centered on jobs related to statistics. This is a great place for data science jobs with a heavy focus on statistics.
  • DataJobs: As the name suggests, DataJobs is a job board designed for finding data science and data analytics jobs.

2. Network on social media platforms and email

Networking is a great way to find data science jobs, especially ones that are related to the industry and niche that you want to work in. By connecting with the right people and companies, you can find opportunities closely related to your interests and future career goals.

While this can work effectively, these services are saturated with users looking for jobs. Because of this, some networking services are more valuable than others in terms of providing access to good, quality jobs. Aside from good old fashioned email, we’ve listed some of the top platforms for networking below.

Check out some examples:

  • LinkedIn: As a site specifically designed for professional networking, LinkedIn is a great place to network for business purposes. They even have job listings and job search options to help you find jobs.
  • Facebook: While Facebook is commonly thought of as a personal networking site, many people have professional Facebook pages, and businesses run their own pages as well. Using your Facebook as a way to network - when done properly - can be a great way of finding available job opportunities.
  • Instagram: While it isn’t the most traditional method of finding jobs or networking, plenty of people create professional Instagram profiles for their business or professional persona. This can be a great option for connecting with people you’d otherwise never find on traditional networking sites.
  • Twitter: Although you have a limited character count, Twitter can be a great place to network with peers, businesses, and the like.

3. Search company websites

One of the best ways to find a data science job that is ideal for you is to go directly to the source. Find a company that fits your industry and the unique data scientist role you want, and then reach out to them to connect, or follow their job postings to apply when an opportunity comes up.

Finding companies this way can give you a leg up on competition - especially competition that found them via a job board - as you’ve shown a direct interest in their company. This effort shows that you’ve done some research about their business and have considered how you’d fit in with their team and in this role.

Check out some examples:

  • Careers page: Some businesses will list jobs to the public on a careers page. This is a great place to review postings, and a great place to apply to jobs directly.
  • Contact page: Even if you can’t find job listings on a company website, you can still reach out via their contact page. If you are interested in a certain type of role, do some research into what they do and speak to how you would fit and offer value in your message.
  • About page: A company’s about page is a great place to learn about what they do and their culture, allowing you to determine if you’d be a good fit with their company. Not only can you understand what your role would look like, but you can sometimes even see the team and learn about the work environment and values.

4. Meet at conferences and events

Despite the ability to network online easily, in-person connections go further, enabling you to create a deeper, longer lasting connection. Meeting with someone face-to-face makes it easier to remember that person, and allows you to create a more personal connection. When they are considering hiring a new data scientist, you are more likely to come to mind first.

Conferences and other in-person events are ideal networking opportunities. Be sure to treat both people and businesses as equal opportunities. You may meet an incredible manager at a company that doesn’t particularly interest you. That personal relationship could still lead to a data scientist role down the road if they change companies or their team expands.

Check out some examples:

  • SafeGraph events: The SafeGraph Community runs in-person and virtual events, helping you learn about data science applications, use cases, and more, all working towards building your experience and knowledge of data science.
  • Industry conferences: Attending conferences in your specific field will help you connect with professionals and businesses in your industry. They are also great places to learn about market standards, best practices, and changes.

5. Join a community of data scientists

Joining a private or public community forum of data scientists is a great way to network and find jobs. Share your experiences, research, and publications, and challenge each other to gain inspiration and fresh ideas.

While you may not be able to apply directly for jobs in these communities, they are an ideal method of networking, as you know members of the community are interested in and have experience in your field, industry, niche, or even the specific job you’re interested in. These are ideal connections for networking, and there are often members of other organizations there as well, helping you gain roles outside of the organization running the community.

Check out some examples:

Example of a business instagram
  • SafeGraph Community: A collective of data scientists and like-minded individuals that are using data to enrich and empower their analytics.
  • IBM Data Science Community: Join discussions based on the type of information you want, including AI skills and hands-on learning.
  • Kaggle: More commonly recognized as a data science tool, they’ve grown a large community and have cultivated a high-quality resource pool.
  • Open Data Science: Gain access to data science and AI news, and connect via their Slack community.

How to apply for data science jobs: 7 tips

Applying to data science jobs can be challenging; some companies are asking for very specific skills while others loosely list technical requirements. With such a wide range of applications, use cases, and industries, data science is a broad field with many roles. 

Therefore, a major part of applying to jobs and finding success with this process is making sure you are applying for the right roles for your goals, experience, and expertise.

Below are the top things to consider and leverage when applying to data science jobs for a better chance at landing the job.

1. Format your resume to be read by applicant tracking systems (ATSs)

Different resume formats and layouts

Many places - especially data science companies - use ATSs (applicant tracking systems) to automatically parse resumes and identify top candidates for interviews based on specific fields. If your resume isn’t properly formatted to be read by an ATS, you could miss out on opportunities without ever being seriously considered - or manually reviewed.

One of our team members used LaTeX, a typesettings system for high-quality technical papers, for their resume. Their resume looked awesome, but they weren’t getting any traction. When they put their resume through an online ATS checker, they got a horrible grade back, indicating that the ATS could not read the resume well. In fact, it couldn’t even read bullet points correctly, likely resulting in the resume being overlooked.

Ensure that your resume follows an easily readable format, and that it’s designed so that ATS systems can easily parse your information.

2. Each data scientist role is unique

Within the world of data science, there are a variety of types of jobs and specific roles. Broadly speaking, there are a few areas of focus, such as statistics, visualization, data management, data architecture, and machine learning. However, there are in fact many more than this.

A data scientist role can focus on any - and all - of these roles. Because of this, it’s important to understand the role you’re applying for and the responsibilities that come with it. Depending on your experience and interest, you may be a great fit for one data scientist role, while completely uninterested in another.

It’s also important to remember that data scientist, data analyst, and similar variations often refer to similar roles and responsibilities. There can be significant overlap between these roles and sometimes very similar roles will be labelled very differently. You may even find project management or business operations jobs closely related to data that suit your interest best.

3. Data science roles vary greatly across industries

Data science industries

More than just differences in data scientist roles, there are many differences in the requirements, responsibilities, and opportunities across industries and sectors.

A data scientist at a large company like Apple may have an extremely niche role at any given time, but they may also have increased opportunity to move around to many niche roles throughout their time with the company. Alternatively, a data scientist at a startup may be expected to wear many hats as projects demand. Further still, some roles may require domain expertise, such as in energy, biology, or even customer service.

4. Understand where you are and plan for where you want to be

With so many unique data scientist roles, there is a wide range of opportunities in your field. When applying to jobs, it’s important to have a clear understanding of where you are and where you want to be in your future career.

Do you want to get a solid understanding of the various areas of data science with the goal of moving up in the organization? Do you want to specialize as an expert in a specific niche area of data science, such as machine learning or geospatial data?

Planning for your future outcomes will help you determine the best role for you, as well as help you ensure you steadily work towards your end goal throughout your career.

5. Curate — and leverage — your online presence

Social media network for your online presence

Your online presence - whether employers admit it or not - is a key component of the hiring process. Interviewers will likely look you up online to see what your online presence is like, and the type of content you engage with. How you represent yourself online can speak volumes about who you are as a person and employee.

When applying to data science jobs, be sure to curate your online presence accordingly. Keep profiles on job-finding apps like LinkedIn up-to-date, and be conscious of how you represent yourself on personal accounts as well. If you have one, make it easy for employers to access your online data science portfolio, showcasing your work.

6. Understand yourself and the role you want

With such a variety of data science roles, to find the best fit for you, you need to understand yourself, the type of industry you want to work in, and the type of work you want to do regularly. 

When you have decided you’d make a good fit and want to apply for a role, be sure to show them how you fit with your resume, cover letter, and any other communication you have. An email message with an attached resume or cover letter is a great place to write something that may not fit in your formal docs, but could make you stand out.

To find a data scientist job that suits you (and you will want to stay at), you’ll want to have a clear understanding of the things you like to do in the field of data science, and the industries that interest you the most. With such a wide spectrum of options, finding the right fit is best for ensuring that you apply for - and get - the right job for you.

7. Feature your work in an online portfolio

Develop a work portfolio

Having a data science portfolio that can be easily accessed by employers is a great way to showcase your work. Whether you have years of experience or you simply have projects from school that you can use as examples of your quality of work, a portfolio speaks volumes about yourself and how you present yourself.

Include an easy to use link to your portfolio on your resume so your interviewer can easily pull it up and review it. Make sure that it’s presented cleanly and that it works, or this could cost you a job right away.

Given the wide range of roles in data science, applying to data science jobs can be a challenging process. You’ll need to identify which roles fit with your experience, background, and interests, which industries are most appealing to you, and what skills are most applicable to the jobs you want before you can even apply.

Once you’ve identified the roles that interest you the most, you can develop a targeted resume and cover letter. This will allow you to develop a more targeted application and increase your chances of success.

Learn why data scientists choose SafeGraph data for their analysis, and practice using these datasets yourself to brush up on your skills, and join the SafeGraph Community to get tips from real data scientists!

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