Driven by the increasing importance of data in decision-making across various industries, the demand for data science professionals is growing rapidly. As the World Economic Forum reports, the need for AI and machine learning specialists is expected to increase by 40% by 2027, while the demand for data scientists, analysts and engineers is projected to grow by 35%. This surge creates numerous opportunities for data science entry level jobs – the topic which we will cover in this blog.
For beginners, entering the field of data science, the opportunities are vast, with different available roles in various industries. In this article, we will guide you on how to find and secure an entry level data science job in 2024. We will help you understand the dynamics of the field to help you set on a successful career path.
Entry-Level Data Science Jobs – Overview
Data science entry level jobs are for individuals who are just beginning their careers in this field. Typically, it is for candidates who have recently completed a degree in data science, computer science, or a related field.
Responsibilities in data science jobs as entry level usually include collecting and cleaning data, conducting data analysis and supporting senior data scientists in developing algorithms and models. These roles may also involve creating data visualizations and reports to stakeholders. Entry level positions in data science let new professionals develop the technical and analytical skills to advance in the field.
It is a good chance for beginners to build a network and gain mentorship from experienced professionals. Those who excel in an entry level data science job can set the stage for their future growth, moving into higher-paying roles.
Required Skills and Qualifications
Educational Background:
To qualify for data science entry level jobs, usually, a strong educational foundation is required. At least a bachelor’s degree in data science, computer science, statistics or a related field. Approximately 47.4% of data science roles specify a degree in one of these areas.
Besides traditional degrees, specialized certifications and online courses can increase the chances of hiring. Certifications in machine learning, data science or cloud platforms (like Microsoft Azure or AWS) are highly valued.
While formal education is a foundation, continuous learning and staying updated with industry trends, that are constantly changing, are crucial. This way, you will increase your chances of landing a role and advance from data science entry level jobs to more senior positions.
Technical Skills:
To stand out in the job market, the right technical skills are decisive, especially for those seeking data science entry level jobs. Typically, employers expect candidates to have proficiency in programming languages, with Python being the most demanded. In fact, Python was explicitly mentioned in 78% of data scientist job offers in 2023 and dropped to 57% in 2024 but still remains a critical skill for anyone entering the field of data science.
Besides Python, a good knowledge of R and SQL is often required, as those languages are for data manipulation and querying databases. In particular, SQL is indispensable for handling structured data, a common task in many data science jobs at the entry level.
The knowledge of machine learning is also highly valued, even at the entry level, data science tasks require professionals to understand the basics of machine learning algorithms and how to apply them in predictive modeling and pattern recognition. Starting data scientists should be able to present their findings in a clear way, so knowing data visualization tools like Tableau or Matplotlib is also important.
Soft Skills:
Soft skills are equally important for success in data science entry level jobs. As data scientists often need to explain complex technical concepts to stakeholders, strong communication skills are essential. They should be able to translate data findings into actionable insights that can make business decisions easier.
Another critical soft skill for entry level data scientists is problem-solving abilities. As they often encounter challenges, that can include data quality issues or incomplete datasets, so they should think creatively and strategically to find solutions.
For entry level data science jobs, teamwork abilities are also essential, as data scientists typically work in collaborative environments with other professionals. They should be able to contribute ideas and listen to others to build a productive and positive workplace.
By developing and demonstrating these soft skills, candidates increase their chances of securing entry level data science jobs remote or on-site. These skills together with technical abilities, make them well-rounded candidates in data science.
Where to Find Entry-Level Data Science Jobs
Online Job Portals:
Websites like LinkedIn, Indeed, AI Talent Flow, and Glassdoor are popular online job portals where companies frequently post data science entry level jobs. On these and other portals, you can filter jobs by location, remote work options and salary.
In order to maximize your chances, you can search by using specific keywords such as “entry-level data scientist” or “entry level data science jobs remote”. These portals also have job alerts to keep you updated on new postings that match your criteria.
Make sure your profile is fully optimized, with detailed skills and a clear headline, in order to increase your visibility to recruiters who might be searching for candidates with similar qualifications to yours.
Company Websites:
Another way to find data science jobs entry level is by checking the career pages of companies in the data science field. Many of them prefer to list job openings on their websites, before posting on third-party portals. Also, applying on a company website, means you found opportunities earlier, giving you a competitive edge.
Large tech firms like Google, Microsoft, and IBM, as well as financial institutions like JPMorgan Chase and Goldman Sachs, often have dedicated pages for data science roles. Check them regularly and apply as soon as positions get listed.
When applying, make sure your application and highlighted skills are tailored to the specific job. Do your homework, demonstrate knowledge of the company’s initiatives and projects, show that you’re genuinely interested in the role.
Networking:
Networking is a powerful way for finding a career path, especially landing data science entry level jobs. Build connections with the field professionals, it can lead you to job opportunities that might not be advertised publicly. By attending industry events, such as data science conferences, workshops and meetups, you will interact with potential employers and colleagues.
It is possible to network through online platforms like LinkedIn, where you can join professional groups where data scientists share advice, resources and also job openings. By actively participating in these communities, you will stay informed about the industry and may also encounter entry level data science jobs.
Besides networking, reach out to professionals in data science for a casual conversation about their career path, to gain valuable insights for your future career. Networking is not only asking for a job, it is building relationships that can support your long-term career growth in data science.
University Career Centers:
Another invaluable resource for recent graduates seeking data science entry level jobs is university career centers. They usually offer personalized support, for instance, resume reviews, mock interviews and job search tips. University career centers often have exclusive partnerships with companies that recruit from their pool of graduates. Career centers also host workshops, job fairs and networking events, and many universities maintain strong alumni networks that can be very useful for job seekers.
Use these resources effectively and you will have more chances of landing a data science job, whether you’re looking for on-site roles or entry level data science jobs remotely.
Types of Entry-Level Data Science Jobs
Data Scientist Intern:
Role: A Data Scientist Intern is an entry-level position that gives starting professionals a chance to have hands-on experience in data analysis, modeling and visualization. Interns usually work under the supervision of experienced data scientists, assisting them with projects of cleaning and analyzing data and developing predictive models.
Skills Required: Programming languages like Python or R on the basic level, familiarity with data analysis tools and strong foundation in statistics and machine learning concepts. Highly valued is strong communication skills for effectively presenting findings.
Average Salary: $40,000 – $60,000 per year (pro-rated for internship duration)
Junior Data Scientist:
Role: As one of the most common data science jobs at the entry level, the Junior Data Scientist is responsible for supporting senior data scientists in the development and application of data models. Typical tasks include collecting, processing and analyzing large data and helping to create algorithms for machine learning that predict future trends.
Skills Required: SQL, Python and R are crucial. Candidates should also understand machine learning algorithms and statistical analysis techniques. Data visualization tools like Tableau can also be beneficial.
Average Salary: Salary: $60,000 – $80,000 per year.
Junior Data Engineer:
Role: This is one of the most technically focused data science entry level jobs and is ideal for those who enjoy working on the infrastructure side of data science. They are responsible for building and maintaining pipelines that move data from sources to storage systems, in order to be accessed by data scientists and analysts. A Junior Data Engineer works with big data technologies, optimizes data workflows and ensures it is available for analysis in a timely and reliable manner.
Skills Required: Programming languages like Python or Java, together with SQL are essential. Candidates should be familiar with big data frameworks, for instance, Hadoop or Spark. Understanding of cloud platforms Azure, AWS or Google Cloud can also be an advantage in this role.
Average Salary: $70,000 – $90,000 per year.
Junior Data Analyst:
Role: This position is a common entry level data science job and offers exposure to data interpretation and business decision-making. Junior Data Analysts are responsible for interpreting data and providing insights to businesses. They clean and collect data, conduct basic statistical analysis and create visualizations of findings.
Skills Required: Experience with data visualization tools, also proficiency in Excel and SQL are critical. Analytical thinking and attention to detail are a must, as everyday tasks include working with large datasets and extracting meaningful information from them.
Average Salary: $60,000 – $80,000 per year.
Junior Data Modeler:
Role: This role is more specialized than other data science jobs entry level and is suitable for those interested in database management and architecture. It focuses on creating data models that represent complex data relationships within an organization. These models are responsible for structuring and organizing data, intending to make data easier to analyze.
Skills Required: Database design principles and experience with SQL are the most wanted skills. Strong understanding of tools and techniques of data modeling, such as RD (Entity-Relationship Diagrams). The advantage can be familiarity with cloud data solutions.
Average Salary: $65,000 – $85,000 per year.
Junior Database Administrator:
Role: If you are interested in the operational side of data management, then the Junior DBA role is a perfect entry level data science job for you. Junior DBA is responsible for maintaining and managing databases. They make sure databases run efficiently and securely. Common tasks include monitoring performance, data backup and troubleshooting if any issues arise.
Skills Required: Candidates should have a strong understanding of database management systems, for instance, MySQL, Oracle or SQL Server. Problem-solving skills and attention to detail are essential.
Average Salary: $60,000 – $80,000 per year.
Junior Machine Learning Analyst:
Role: This is one of the most technically demanding data science entry level jobs. Junior Machine Learning Analyst focuses on developing and applying machine learning algorithms to analyze big data and make predictions. Tasks involve working with experienced data scientists to implement predictive models and interpret their results.
Skills Required: Proficiency in programming languages and a strong understanding of machine learning libraries such as scikit-learn or TensorFlow are vital.
Average Salary: $70,000 – $90,000 per year.
Tips for Landing an Entry-Level Data Science Job
Building a Strong Resume and Cover Letter:
In any job search, a strong resume and cover letter are crucial and data science entry level jobs are no exception. The resume should clearly demonstrate relevant skills, together with education and any hands-on experience, such as internships or related projects. As for the data science field, proficiency in key programming languages like Python, R and SQL, as well as familiarity with data analysis and tools for machine learning, are a must for any entry level data science job.
A cover letter should emphasize a passion for data science and explain how the candidate’s background makes them suitable for the specific role. Each cover letter should be catered to the job description and should not be generic.
Creating a Portfolio:
A portfolio for an entry level data science job should include projects that showcase the candidate’s ability to apply data science techniques to real-world problems. Common examples of entry level candidates‘ portfolios include data cleaning and preprocessing tasks, which can also be exploratory data analysis, machine learning models and data visualizations.
Clearly explain the problem you addressed, the methods you used and the results you achieved for each project. Include links to code repositories, where employers can review your work in detail.
Networking and Mentorship:
As already discussed above, networking is essential for breaking into data science jobs entry level. Building connections with field professionals can lead to job opportunities and valuable advice. That is why you should attend industry events and do online networking by joining groups on LinkedIn. Staying engaged in these communities will help you be up to date about job openings and industry trends.
Another incredibly beneficial tip is to find a mentor in the field. They can provide guidance on career development and give you feedback on projects. Establish these relationships to gain an advantage as you pursue entry level data science jobs.
Preparing for Interviews:
The first thing you should do when preparing for data science entry level job interviews is to research the company and understand its data-related challenges and objectives. Be well-prepared to discuss how your skills and experience align with the company’s needs.
It is no surprise that you may be asked technical questions about programming, statistics and machine learning. Most likely, an interview will include coding exercises and case studies. Do a lot of practice in solving common data science problems and explaining your thought process clearly. Be prepared for behavioral questions too, recruiters assess your problem-solving abilities by that.
Try not to stress, this is your opportunity to show potential employers that you are capable of handling the responsibilities of an entry level data science job.
FAQs on Entry Level Data Science Jobs
Can I get a data science job without experience?
Yes, if you demonstrate your skills through internships or certifications it is possible to get data science entry level jobs. To compensate for the lack of professional experience, gain relevant skills through courses and certifications, and build a portfolio of your data analysis projects and machine learning models, these will help you stand out.
Is it hard to get an entry level data science job?
It can be challenging to get an entry level data science job, but not impossible. Employers seek candidates with strong technical skills in programming, machine learning and data analysis, together with practical experience. Persistence and continuous learning will help you land your dream job.
Is data science hard for beginners?
Data science can be hard for beginners, as it requires a very deep understanding of programming, statistics and data analysis. However, with the right resources and dedication, even beginners can build the necessary skills to succeed in data science entry level jobs.
How can a beginner start data science?
Start by learning the basics of programming (especially Python), statistics and analysis. There are a lot of online courses and tutorials that will help you build foundational knowledge. Work in real-world projects that let you analyze datasets or create simple machine learning models. Build a portfolio that showcases these projects. Network and join data science communities to gain valuable insights and job leads.
Is 1 year enough for data science?
One year can be enough for data science entry level jobs, but it depends on the intensity and focus of your learning. If you dedicate enough time to studying programming, statistics and machine learning, and you complete projects to apply these skills, then you can be prepared for it. However, the field of data science evolves rapidly and you will need continuous learning and practice.
Can I become a data scientist in 3 months?
It is very unlikely to become a fully proficient data scientist in just 3 months. However, what you can do in 3 months is to acquire basic skills in data analysis and machine learning, with programming, that can be enough to start applying for entry level data science jobs remote or on-site.
