A career in data science provides incredible employment opportunities at all levels of experience. The challenge here is obtaining one for yourself, especially if you are a novice. To qualify, you must possess critical technical skills, in addition to a talent for statistics.
The article aims to discuss the best entry-level data science jobs and how to get them. While considering their responsibilities and required skills, we will examine the highest-paying entry-level data scientist roles and their associated middle and senior roles for career advancement.
What Is Data Science and Why Is It Important?
Data Science is the study of extracting knowledge, deriving actionable insights, and generating leads from big data. Data scientists can learn from data and provide solutions to real-world problems by using statistical methods, programming languages, and a set of algorithms.
Data science is important in many organizations and spans almost all industries. Businesses use knowledge from this field to discover market trends, understand customer behavior, and develop predictive models to improve their quality of service.
What Is the Job Outlook for Data Scientists?
According to the US Bureau of Labor Statistics, the employment of data scientists in the category of computer and information research scientists is expected to increase by 22 percent by 2030. Surprisingly, this value exceeds that of other occupations in the US economy, which is expected to grow by 8 percent during the same period.
With such a projected increase in employment, it is no surprise that data scientists are in high demand. The sole reason is increased adoption and integration of data science in business operations. Furthermore, as technology advances, data science professionals will attract the attention of employers who have embraced digitization to improve their customer services.
Top Reasons to Pursue a Career in Data Science
- Increasing demand for data scientists. More organizations incorporate data science into their operations, considering how much information is in big data. Also, digitization has paved the way for more data science professionals to enter the corporate world.
- Impressive salaries and benefits. Data scientists enjoy great remunerations and job benefits like health insurance, paid vacations, and bonuses depending on the employer. There are also exciting opportunities with an excellent work-life balance.
- Ever-evolving field. A data science career allows you to work in a fast-paced field with numerous opportunities. It offers opportunities for advancement while also meeting the needs of the industry.
- Opportunities for a career change. Data science skills can be applied in various fields. They are relevant in ecommerce and finance, but they are also applicable in healthcare and telecommunications.
- Minimal competition. Since data science is still in its early stages, there is little competition for positions. However, as more businesses adopt it, now is the ideal time to begin your career in the industry.
What Does an Entry-Level Data Scientist Do?
Data scientists in entry-level positions are responsible for gathering, cleaning, processing, and managing data using various software. However, their duties vary depending on the scale of the organization. Small organizations could handle the entire data processing operation, from collection to building predictive models. However, the process chain is segmented in large organizations, and scientists’ responsibilities are more streamlined.
Types of Entry-Level Data Science Roles
Entry-Level Machine Learning Analyst
Machine Learning Analysts conduct statistical analysis, maintain artificial intelligence (AI) systems, and manage machine learning processes. They are responsible for ensuring the accuracy of predictive models derived from a wide range of data.
Entry-Level Business Analyst
Entry-level business analysts work closely with superior analysts in planning, budgeting, and evaluating business plans. They also collate and document information to create roadmaps that align with the blueprint of a business model.
Entry-Level Data Engineer
Entry-level data engineers are responsible for developing, maintaining, and managing data feeds and pipelines. They fix bugs and flaws in data systems to ensure that accurate information is extracted for business purposes.
Common Responsibilities of Entry-Level Jobs
- Discover value data sources
- Develop automated extraction systems
- Clean and process structured and unstructured data
- Identify patterns and trends from data analytics
- Employ machine learning principles to build algorithms and predictive models
- Utilize visual elements and techniques to make presentations
- Suggest solutions and work with product teams to develop new business processes
How Much Do Entry-Level Data Scientist Jobs Pay?
Entry-level data scientist jobs pay $68,054 per year, according to ZipRecruiter. This mean value implies that pay varies depending on the employer. According to the report, some scientists could earn up to $83,000 per year, while others could earn $55,000. In any case, these figures indicate adequate pay for entry-level data science positions.
Entry-Level Data Scientist Jobs That Pay the Most: Overview
|Job||Average Salary||Min. Education/ Training||Associated Mid and Senior Roles|
|Machine Learning Scientist||$119,889||Bachelor’s degree in computer science, mathematics, or related field||Machine Learning Engineer, Senior Machine Learning Scientist|
|Algorithmic Trader||$117,463||Bachelor’s degree in computer science, statistics, finance, or related field||Quantitative Analyst, Senior Algorithmic Trader|
|Business Intelligence (BI) Developer||$85,120||Bachelor’s degree in computer science, or related field||Business Analyst, Senior BI Developer|
|Data Engineer||$75,858||Bachelor’s degree in computer science, software engineering, applied math, or related field||Software Engineers, Lead Solutions Architect|
|Technical Analyst||$63,218||Bachelor’s degree in computer science, software engineering, or related field||Business Analyst, Senior Technical Analyst|
|Database Administrator||$62,008||Bachelor’s degree in information science, computer science, or related field||Information Systems Analyst, Senior Database Administrator|
|Business Analyst||$57,531||Bachelor’s degree in business management, information technology, or related field.||Product Manager, Senior Business Analyst|
|Data Architect||$58,742||Bachelor’s degree in computer science, information technology, or related field||Solutions Architect, Senior Data Architect|
|Junior Marketing Analyst||$44,754||Bachelor’s degree in computer science, statistics, business administration, related field||Senior Marketing Analyst, Marketing Data Scientist|
|Data Analyst||$43,250||Bachelor’s degree in statistics, economics, or computer-related field||Business Analyst, Senior Data Analyst|
Best Entry-Level Data Science Jobs: A Closer Look
Machine Learning Scientist
Machine learning scientists harness the intelligence of computers. Since they are relatively new, they are scarce and in high demand across several industries. As a result, they have amazing benefits and opportunities for advancement. It’s no surprise they’re the highest-paid data scientists.
Machine Learning Responsibilities
- Select appropriate data sources and automate data extraction
- Develop machine learning systems and algorithms
- Implement predictive models using precise data analytics
Algorithmic trading involves analyzing financial securities for high-frequency trading. It improves decision-making by combining statistical models with critical thinking. Algorithmic traders can use computers to execute trades based on programmed instructions.
Algorithmic Trader Responsibilities
- Perform statistical analysis on currency assets and instruments
- Develop programs to execute actions using programming languages
- Leverage high-frequency trading technology to enhance decision quality
Business Intelligence (BI) Developer
Business intelligence generally involves different strategies and technologies that enable organizations to make better decisions. Professionals in this field collect data from different aspects of the business to improve operations, identify better opportunities, and assess employee productivity.
Business Intelligence (BI) Responsibilities
- Present valuable information to decision-makers using data analytics
- Employ technologies to organize data points
- Identify growth opportunities for business operations
Data Engineer Responsibilities
Data engineers hold a more technical position compared to most data science disciplines. They are concerned with developing models and ensuring that structures align with business needs. It is an exciting field because it combines scientific principles and engineering processes.
- Ensure data integrity build data pipelines
- Develop information systems and writing algorithms
- Deploy models and programs to automate data processing and presentation
Technical analysts are relevant in various industries including, finance, healthcare, and tech. They are experts at extracting reliable information and gaining insights from interviews. A degree in information technology is typically required, but technical skills such as programming are invaluable in this role.
Technical Analyst Responsibilities
- Collect information to bolster operations from surveys, interviews, and research
- Analyze business requirements for projects
- Coordinate projects and act as a middleman between managers and employees
Database administration involves managing, securing, and maintaining data. Database administrators ensure the integrity and availability of data when necessary by using tools such as Oracle, IBM DB, and MySQL.
Database Administrator Responsibilities
- Frequently update database
- Manage and maintain data structures
- Assist and coordinate the development of database systems
Business analytics relies heavily on data. Analysts can identify business needs and cater to them using related principles. While the solutions they provide may include some software deployment. They may also include a policy change or organizational restructuring in some cases. However, business analysis facilitates strategic business operations.
Business Analyst Responsibilities
- Conduct in-depth analysis to identify business opportunities and challenges
- Proffer and deploy solutions to meet requirements
- Assist in planning, budgeting, and risk analysis
Data architecture is concerned with the integrity of the entire database structure of an organization. Architects in this field use models, programs, or principles to provide IT support for database systems, from data collection to processing to data management.
Data Architect Responsibilities
- Design and install database solutions
- Examine structures for flaws and implement adequate correction measures
- Organize teams to provide project deliverables
Junior Marketing Analyst
Marketing analysis studies various subjects comprising customer behavior, product conversions, and regional engagement with products. The aim here is to determine recurring trends and patterns to enhance decisions beneficial for customers and organizations.
Junior Marketing Analyst Responsibilities
- Discover market trends and patterns
- Study customer behavior and engagement with product
- Identify new market opportunities for growth performance
Data Analytics is versatile and applicable everywhere. It takes a less technical approach than data science and other related fields. It entails analyzing raw data and deriving actionable insights to improve decision-making.
Data Analyst Responsibilities
- Examine and analyze datasets to discover trends
- Design and manage automated data systems
- Communicate findings and proffer solutions to stakeholders
What Types of Companies Hire Entry-Level Data Scientists?
Data science is applicable in many fields thanks to the synergy of technology and business. Employers from various sectors, including business and finance, telecommunications, digital marketing, healthcare, and cybersecurity, require the technical expertise of data scientists. Major employers include HSBC, Netflix, Google, Amazon, Oracle, and Facebook.
Career Progression: Mid and Senior-Level Data Science Roles
Mid-Level Data Science Jobs
- Average salary: $82,343
- Related entry-level roles: Technical Analyst, Business Intelligence (BI) Developer
- Responsibilities: Conduct market analysis, build and maintain data models, evaluate business performance, perform quality assurance, and implement new strategies
Information Systems Analyst
- Average salary: $84,444
- Related entry-level roles: Database Administrator, Data Analyst
- Responsibilities: Conduct research, develop plans and policies, test standards, and design information systems
- Average salary: $99,729
- Related entry-level roles: Data Engineer, Data Architect
- Responsibilities: Write and test program codes, build automated data processing systems, execute complete software development life cycle (SDLC), implement and maintain software solutions
- Average salary: $118,652
- Related entry-level roles: Algorithmic Trader, Business Analyst
- Responsibilities: Conduct statistical analysis on financial securities, develop predictive models, build systems with enhanced trading strategies, manage organization portfolio
Machine Learning Engineer
- Average salary: $130,530
- Related entry-level roles: Machine Learning Scientist, Data Engineer
- Responsibilities: Examine data sources and data sets, develop machine learning algorithms and systems, conduct adequate research, run tests, and regularly maintain ML systems
Senior Data Science Jobs
Senior Data Analyst
- Average salary: $100,739
- Related entry-level roles: Data Analyst, Database Administrator
- Responsibilities: Extract and collate data, conduct precise data analysis, discover patterns and market trends, coordinate data processing techniques
Lead Solutions Architect
- Average salary: $128,160
- Related entry-level roles: Data Engineer, Data Architect
- Responsibilities: Build technical solutions, implement and monitor data platforms, identify new technology to adopt for improved business strategies
Marketing Data Scientist
- Average salary: $139,015
- Related entry-level roles: Junior Marketing Analyst, Business Analyst
- Responsibilities: Study markets trends, derive strategic insights, experiment hypotheses, build models to enhance financial decisions
Senior Data Architect
- Average salary: $146,109
- Related entry-level roles: Data Architect, Data Engineer
- Responsibilities: Optimize data sources, develop data architecture systems, oversee automated data processes, identify structural data flaws, install adequate data solutions
Senior Machine Learning Scientist
- Average salary: $182,999
- Related entry-level roles: Machine learning Scientist, Data Engineer
- Responsibilities: Leverage data sources, build predictive models and algorithms, develop machine learning systems for better decision making, communicate finding to teams
How to Get a Job in Data Science
While there are numerous opportunities with little competition, finding a job in data science may be difficult. Employers typically prefer a bachelor’s degree, but in some cases, an advanced degree such as a master’s or PhD is required. Considering the time it takes to complete a college degree, attending a data science coding bootcamp would be a good alternative. You can also obtain certifications to strengthen your candidacy.
In addition, LinkedIn recommends that you have excellent technical skills. To attract the attention of large employers, prospective data scientists must have a combination of core data science skills and soft skills. More importantly, they influence your job performance and long-term career success.
With that in place, you can begin applying for jobs. You should visit job boards and companies’ career pages, as well. Pay attention to the details in job postings and craft an effective cover letter and resume, accordingly. You can also attend career fairs, which allow you to network with professionals in the field.
Required Skills for an Entry-Level Data Scientist Job
- Math and Statistical skills: Data science involves working with a lot of numbers. Hence, you should have a good understanding of statistical principles and know how to apply them.
- Big data skills: With increased data generation, there is a need to store and manage data effectively. Scientists should develop and maintain secure data systems to optimize information.
- Analytical skills: Data scientists should be able to gather and analyze data. They can reveal patterns and offer technical solutions to meet business needs by asking the right questions.
- Communication skills: This skill comes in handy when presenting your findings to teams and stakeholders. Data visualization techniques are effective ways to communicate research and analysis findings.
Top Certifications for Entry-Level Data Scientists
- Certified Analytics Professional (CAP): This certification will introduce you to the intricacies of data analytics. Although it does not include the commercial aspect, you will gain a good understanding and relevant knowledge of the core principles of data analysis.
- Open Certified Data Scientist (Open CDS): This certification is valid for a lifetime and comes with a unique structure. It follows a review-and-merit structure rather than coursework and tests. Qualified applicants must have professional experience and have earned three levels of certification: Certified Data Scientist, Master Certified Data Scientist, and Distinguished Certified Data Scientist.
- Google Data Machine Learning: This certification serves as a window to examine data analytics through the Google Cloud Platform. It involves three tracks and extends to data science and machine learning. It is valid for a lifetime.
- SAS Certified Data Scientist: This certification involves two tracks and focuses on data manipulation and management. It also includes data visualization technique modules and does not expire.
- SAS Certified Big Data Professional: This certification comprises nine courses that cover a broad scope of data analytics, from the fundamentals of statistics to building systems to effective communication. This program requires a strong understanding of the Statistical Analysis System (SAS) or programming. It is also valid for a lifetime.
Can You Get an Entry-Level Data Science Job with a Coding Bootcamp?
You can get an entry-level science job by attending a coding bootcamp. This certification comprises nine courses covering a broad scope of data analytics, from the fundamentals of statistics to building systems to effective communication. Broad knowledge of Statistical Analysis System (SAS) or programming is essential to this program. It is also valid for a lifetime.
"Career Karma entered my life when I needed it most and quickly helped me match with a bootcamp. Two months after graduating, I found my dream job that aligned with my values and goals in life!"
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Can You Get an Entry-Level Data Science Job with a Coding Bootcamp?
A coding bootcamp is an appropriate alternative to a formal degree. It provides a more direct path to becoming a tech expert without attending college. Within months, you’ll acquire the necessary skills for your dream data science job.
Furthermore, a bootcamp is an excellent way to get an entry-level position because students have access to career mentorships and are often hired immediately by affiliated companies.
Top Data Science Bootcamps
- Flatiron School
- Le Wagon
- General Assembly
Best Resources for Data Scientists
Resources For Learning The Rudiments
Without a basic understanding of what the field entails, you will be unable to advance in your data science career. There are numerous online resources available for this purpose, which are divided into various categories. Massive Open Online Courses (MOOCs) offered by Coursera, edX, and Udemy are excellent options. Alternatively, you can consult textbooks, Youtube, or even attend a bootcamp to build a foundational knowledge of data science.
Resources For Honing The Skills
Data science is a highly practice-oriented field that requires the constant application of technical skills. Since practice makes perfect, you should use tools like SAS, MATLAB, Apache Spark, and Excel. They have powerful features that allow users to explore data sources, conduct in-depth analysis, build machine learning models, and eventually provide solutions to real-world problems.
Resources For Collaboration And Networking
An effective way to sharpen your technical skills and increase productivity is by collaborating with teams on data science projects. Platforms like GitHub, Kaggle, IBM Data Community, and Reddit provide users the opportunity to network, share ideas, and learn from one another. They can also serve as organizational and portfolio-building tools.
Is a Career in Data Science for You?
That decision is yours to make. However, if you have a knack for numbers, you will most likely enjoy a career in data science. It is suitable for those who can objectively analyze results using critical thinking skills. With the growing global demand for data and its impact on technology and business, many jobs have competitive benefits and opportunities for advancement in this field.
Entry-Level Data Science Jobs FAQ
An internship is a great way to gain industry experience and learn how to apply technical skills in the real world. Volunteering and personal projects are also excellent ways to gain relevant experience for potential employers to consider your candidacy. Overall, ensure that you develop proficiency in the critical skills that will determine your career success.
It varies from one employer to another, and the company’s scale also matters. However, entry-level data scientists are responsible for identifying reliable data sources, extracting and processing data, and discovering hidden patterns and trends. They also develop algorithms and predictive models while working with various teams to provide technical solutions.
Data scientists can work from anywhere. The nature of their job doesn’t necessarily require an office space. Furthermore, they perform most of their tasks on a computer, allowing them to deliver effectively while working remotely. However, there’s an increased number of meetings compared to the traditional working style. Effective communication is critical for any remote work.
Generally, it takes twelve weeks to about six years, depending on the route you follow. You can learn data science skills in just a few weeks if you attend a bootcamp. However, going the college route will take you three to four years to obtain a university degree. However, those pursuing advanced degrees, such as a Master’s in Data Science, may have to wait an extra year or two.
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