Data science jobs are among the most in-demand and highest paying tech jobs. But the competition has never been steeper. Tech layoffs mean that the market is flooded with talented but unemployed tech professionals. Job postings can get hundreds of applicants as soon as they’re posted. Entry level data science jobs are especially competitive with the rise of free data analysis certificates and courses. All while entry level data science jobs (especially analysts) are being eliminated in place of specialized and senior data roles… or supposedly entry level data science jobs with senior level requirements. How can you grab the attention of recruiters and hiring managers?
We help aspiring data scientists land their dream jobs every day. Using this experience, we compiled a list of top tips and suggestions so you can find entry level data science jobs more efficiently.
Step 0: Master In-Demand Data Science Skills
Undergraduate degrees usually don’t give you all the specific in-demand skills you need to land a data science job or perform well on the job. Even data analyst roles, which are not technically rigorous compared to data science roles, require extremely specific skills like SQL, R, and Python. Unless you learned these on the job or in a specialized computer science course, you may not have learned the skills you need.
Online certificates and self-study
A wide variety of free online certifications and courses are available to teach you the core data science skills you need like Python. While certificates alone are not enough to get you a job or train you completely, they prove to employers that you have the core skills. Data science certificates can be especially helpful for people with unrelated undergraduate degrees, or no degrees at all. These can be a great refresher for technical data science interviews, helping you ace Leetcode questions and perform well in technical data science interviews.
Online Masters
One of the top jobs for career changers, you can become a data scientist (or analyst) by building on your undergrad study. Even if you majored in something unrelated to data science, graduate programs like Georgia Tech Online Masters and UC Berkeley online masters will accept you into their graduate programs, provided you had a relatively high GPA and a willingness to self-study. This can be an especially good fit for people who want to become senior data scientists right away rather than work in data analysis.
Luckily, online data science masters are much cheaper than most masters degrees. Prices range from $10k to $40k for 2 years. However, the course load can be intense. It may take working professionals 3+ years to complete, especially if they have other responsibilities outside of work.
Bootcamps
The top data science bootcamps promise to teach you core skills needed to become a data scientist or data analyst. While there are plenty of free courses online, bootcamps offer accountability and 1-on-1 instruction, as well as expert instructors with industry knowledge. If you want to break into data science as soon as possible, data science bootcamps could be a good fit. However, if you don’t have a technical background, bootcamps are more effective at teaching you data analysis skills than data science skills.
1. Strengthen your resume and online profiles
You need to ensure that your resume, LinkedIn, and GitHub profiles are strong before applying to any jobs. Instead of using grunt statements on your resume, which demonstrate what tasks you did, focus on impact statements, which use numbers to quantify results and show how and why your accomplishments made a difference. You can also describe the scale of a project. The best way to do this is to include information on how many devices you served, tests you considered, scenarios you handled, and more. This gives additional context to your work and helps tell the story of your experience in a more clear narrative.
To help paint a fuller picture of your previous work experiences or projects, you can optimize your LinkedIn by expanding on the information you included in your resume. Your LinkedIn profile should have links to your GitHub profile and additional projects as well. This way, recruiters can access all of your previous work in one place.
Finally, you should not underestimate the importance of a data science portfolio when applying for jobs. Any company worth working for expects you to have a polished data portfolio, even for entry level data analyst jobs.
Ideally while studying, both bootcamp and university grads should have already made a variety of side projects uploaded to your GitHub portfolio. However, GitHub is far from visually striking. You’ll likely be better off using Notion or a custom website. Whether you use Github or a more visually appealing portfolio platform, be sure to include context. Recruiters and hiring managers want to see the impact of your work and why it matters. You can also include an “about me” page to humanize yourself and share some of your hobbies outside of work.
Besides uploading new projects, you can also contribute to existing GitHub repositories and projects. By actively working on your portfolio, you can help differentiate yourself from your peers. This is because your competition will be applying to the same job postings with similar portfolios. Side projects also allow you to highlight your strengths, collaborate with other data scientists, and practice skills that were not covered in your bootcamp’s curriculum.
2. Find the entry level data science jobs and positions that match your skill set and experiences
Although you might not have any direct data science experience, you probably have specialized work experience from previous roles. Even entry level data science roles tend to be specialized by industry. For example, data analysts working with marketing data would benefit from a marketing background. If cleaning healthcare data, healthcare experience can help candidates get interviews.
Different data roles also require different skills. Data engineers may need expert level coding and technical skills, while data analysts may rely more on communication skills to present graphs they created with just Excel and Tableau. Recognize what roles match your specific skill set. Spend time on the applications that fit your background so you can kickstart your data science career.
In general, news grads and career changers seeking entry level data science jobs have 4 options: data analyst, data scientist, data engineer, and business analyst. Those prepared for data analyst roles should have backgrounds in collecting, organizing, interpreting, and creating reports using data. In addition, they should be ready to perform analysis and assist with making effective business decisions. Analysts use tools like SQL, XML, Javascript, and R, as well as machine learning programs, data visualization tools, Hadoop, and more.
For data scientist jobs, you need to be proficient in Python, Java, and R (the most common data science programming languages). You also need advanced skills in math and statistics. They use advanced mathematical and algorithmic techniques to solve complex problems, build analytical tools, identify trends, propose solutions, and more. Proficiency in MATLAB, SQL/NoSQL databases, SPS, and SAS is crucial.
Those interested in data engineer jobs generally have backgrounds in fields such as software engineering, computer science, and information technology. You might begin your career by looking for IT assistant or management roles. Duties include:
- Building and maintaining data pipelines and warehouses
- Developing and constructing architecture with databases large-scale processing systems
- Solving problems using different scripting languages
- Statistical modeling
- And more
Besides mastering the standard data science programming languages, data engineers should be proficient in UNIX, Linux, Solaris, AForge.net, as well as Bigtable, Cassandra, Ruby Perl, C/C++, and more.
If you have experience working at the nexus of data and business, you can consider business analysts positions. These would be a good fit especially if you have a background in business administration, finance, or accounting. Business analysts use diagramming programs, data analysis programs, Hadoop, and SQL/NoSQL databases to evaluate and develop strategic plans for businesses. They must be comfortable using business model analysis, process design, and systems analysis. They also need to communicate with colleagues, management, the IT department and other stakeholders to help reduce costs and create & test new systems.
3. Reach out to hiring managers, recruiters, and fellow new grads
Writing a good cold email to recruiters, hiring managers, and fellow grads can help draw attention to your application and can often be the factor that moves you forward. You can use LinkedIn to find the recruiters and hiring managers who will be evaluating your resume and portfolio.
Try connecting with people who have something in common with you. Sharing an interest or growing up in the same city are good starting places. But, you will probably have better luck reaching out to someone who attended the same bootcamp as you. They are more likely to offer assistance, as they will have a stronger understanding of your background. You can find an email address using tools like Clearbit and Leadfinder. If you prefer to add someone as a connection on LinkedIn, always include a personal note that highlights your connection and expresses your interest in their current position or company.
Another option for new grads is networking with fellow alumni through the bootcamp’s career center. Reach out, introduce yourself, and let them know that you are actively looking for a job. If you are already familiar with the career center, don’t be afraid to update them on your job search. Often, they will direct you to companies that currently employ their graduates. In addition, most bootcamps have alumni groups on Facebook, LinkedIn, Slack, and other online platforms. These can be excellent spaces for sharing job resources and networking.
4. Research the company to prep for behavioral interviews
As you prepare for phone screens and behavioral interviews, you need to develop a strong understanding of a company’s culture, as well as their values, mission, and products. You might have a Facebook account that you use everyday, but you should still research the company before interviewing. Take a look at their About page to see their mission and history. This information can help you understand their goals, achievements, values, and more. Facebook, for instance, includes a page that advocates for promoting safety and freedom of expression. You can mention this value, which is one of the company’s main priorities, in your elevator pitch. In addition, this info should be added to your responses to the behavioral interview questions to emphasize why you are a good fit with their culture and mission.
Use our list of behavioral questions from top tech companies to start thinking through how you would personalize your answers.
5. Study key concepts and work through mock technical interview questions
As a new grad or career changer seeking entry level data science jobs, you probably have spent less time working with data than grads who already have work or internship experience. To prove that you are ready for entry level data science jobs, you need to perform well on your technical interview.
When you prepare, be sure to read practice questions carefully. You don’t want to start solving the problem until you understand each component. When you tackle a mock interview question, brainstorm some clarifying questions you would ask the interviewer before beginning, like “For how long did you collect the data?” or “What does this unit represent and why?”
Interviewers want to understand your logic and reasoning skills, so you should practice explaining your process as you solve mock interview problems. Almost all of the questions on your technical interview will test your knowledge and understanding of the following areas:
- Statistics
- Probability
- SQL/databases
- Programming
- Modeling
- Specific case studies
Carefully study these fundamentals so that you develop a thorough understanding of each one.
To help you get started, we have compiled a list of data science interview questions from top tech companies.
6. Brainstorm questions for your interviewer
Another way to prove that you have an in-depth understanding of the data science field and the company to which you are applying is by preparing questions to ask in a data science job interview. Posing thoughtful questions helps you demonstrate an understanding of the tools and methods that data scientists use, as well as underscore why you are excited about the company’s work culture, values, and products.
By using our suggestions and tips, you can prepare to take the necessary steps to find entry level data science jobs and land a job in data science. If you are interested in optimizing your job search by working 1-on-1 with a mentor and receiving additional guidance on each step of the process, join Pathrise.
Apply today.
