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Data Scientist Salary: How much is it, and where can you get the Best Opportunities

Data scientists are well-paid all throughout the world. In fact, a recent LinkedIn research revealed that the top in-demand and fastest-growing jobs are data science specialists, machine learning engineers, and artificial intelligence specialists. However, a data scientist’s pay is more of a spectrum than a target, just like any other field or role. There are many different variables that affect the average pay. The criteria used to determine what constitutes “high” pay might also vary depending on a person’s location, age, or level of education. Having said that, continue reading if you want to learn more about how lucrative this industry is or determine if you’re getting paid fairly.

Read also; 10 Famous Computer Scientists you should know About

What is Data Science?

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Data science is a field that applies cutting-edge analytics methods and scientific principles to get useful information from data for strategic planning and business decision-making. For businesses to make informed decisions, data science as a function is obviously becoming more and more crucial. There are numerous subfields of data science, including machine learning, artificial intelligence, data mining, data engineering, and others.

What is the typical pay for data scientists?

The typical pay in data science will vary depending on a wide range of criteria, including;

• Experience
• Position/Job Title
• Organization type
• Geography
• Educational History

1. The pay for a data scientist is proportional to experience

Data scientists can be categorized into three classes based on their years of experience and accompanying pay, including;

i. Data Scientist starting out (0-5 years of experience)

Freshmen or newly hired professionals are included in this group. Therefore, entry-level data scientists earn about $95,000 USD annually.

ii. Data Scientist at a mid-level (5-12 years experience)

This group consists of data scientists who have been working in the field for about ten years and may be mid-senior-level experts. A mid-level data scientist would make about US $130,000 annually. As a result, if the individual in question already has a managerial position, his or her pay might increase to US $195,000.

iii. Data scientist at a senior level (12-20 years experience)

This team would include data scientists with extensive industry knowledge and potential leadership positions. So, the average senior data scientist’s pay would be about US $165,000. However, the pay for such a data scientist in a leadership position may reach US $250,000.

Pro Tips

-Experience can be calculated using a data scientist’s knowledge density and depth in addition to their years of experience.
-Years of experience cannot be picked at random; only relevant experience should be taken into account.
-Not all managers make more money than employees; in some specialized positions, talented employees may earn more money than managers.
-Additionally, salaries differ based on roles. For example, data mining positions may not pay as much as data engineering positions.

2. Do job titles affect how much a data scientist makes?

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Data science is not a single position, but rather a group of positions with various responsibilities. Therefore, the job title of a data scientist might vary depending on years of experience, skill capabilities, or the type of company they work for. In essence, the monthly compensation of a data scientist is influenced by the job titles held. Here is an illustration of several data scientist positions and how much each pays.

Job Title                                            Average Salary

Data engineer or architect      US $112,493

Data scientist                                  US $117,212

Data analyst                                     US $69,517

Chief Data Office                          US $146,133

Big Data Engineer                         US $104,463 

Machine learning engineers    US $131,001

i. Data engineer or architect

Data engineers construct pipelines for directing data from various sources. They put a lot of emphasis on the raw data’s production readiness as well as components like formats, robustness, scaling, data storage, and security.

What You’ll Do

These are all included in data engineering;

• Constructing fluid data pipelines for real-time analytics
• Writing sophisticated queries to make sure data is easily available
• Setting up and keeping up data management systems
•  Developing tools for team members to use data analytics
•  Formatting data so that team members can use it

Skills You’ll Need

Data engineers have the following skills;

• Programming languages like Scala, Java, SQL, and Python
• Experience in software engineering, computer science, or a related discipline
• Understanding cloud computing and data architectures
• Extensive experience with automation and scripting
• Knowledge of distributed systems, data APIs, and ETL tools

ii. Data scientist

Poster System Analyst & Data Scientist-by Ammarul-

The duties of data scientists can differ from organization to organization. In general, data scientists gather and analyze the information that a corporation has access to.

What You’ll Do

A data scientist may be responsible for things like;

• Identifying data sources and automating the data retrieval procedure.
• Preprocessing unstructured and structured data to enable analysts to use it to discover insightful information.
• Use analytics tools and machine learning techniques to find trends in data.
• Development of predictive models to identify trends and forecast them, allowing organizations to make crucial business decisions.
• Examining data systems for opportunities for efficiency, security, and optimization.

Skills You’ll Need

The following are some of the skills data scientists possess;

• Manipulating enormous volumes of data
• Writing code in computer programming languages like Python and SQL
• Proficiency in calculus (both single variable and multivariable), probability, linear algebra, and statistics
• Effective communication skills and the capacity to tell a story using data

Read also; 10 of the Most Famous American Inventions

iii. Data analyst 

Data analysts find trends, patterns, and relationships within data to help businesses make decisions. This is a stepping stone into a career in data science. Data analysts collect and organize relevant information, create visualizations, and deliver reports to stakeholders.

What You’ll Do

There are three main areas of duties for data analysts;

• Data mining to obtain knowledge from both primary and secondary sources
• Data cleaning to remove mistakes and conflicts from the data and to uncover insightful patterns, correlations, and trends
• Data visualization, which makes it simple for stakeholders to access the information.

Skills You’ll Need

Data analysis abilities include;

• Experience with data analytics programs like Microsoft Excel, Tableau, Google Analytics, Jupyter, and Amazon Web Services
• Cleaning and mining data
• Storage of data
• Visualization of data
• Effective communication
• Analytical and creative thinking

iv. Chief Data Office 

The management of the company’s data and analytics activities, including the architecture, user requirements, software development, report development, and integration of AI and machine learning, fall within the purview of the CDO.

What You’ll Do

The following are some of the duties of a chief data officer;

• overseeing the architecture, user requirements, software development, report development, and integration of AI and machine learning for the company’s data and analytics operations.

Skills You’ll Need

Among the abilities of a chief data officer are;

• Effective communication.
• C-suite executives need to be empathetic, capable of critical and analytical thinking, and capable of making decisions.

v. Big Data Engineer

Extract, Transform, Load Data Flow Diagram-by Bamyers99-

The infrastructure that enables individuals to gather and organize massive volumes of data is the focus of big data engineering. This enables data analysis on billions of clicks, taps, likes, swipes, shares, and sales. In order to achieve this, they construct data pipelines, plan and oversee data infrastructures, manage data storage, and concentrate on the ETL (Extract, Transform, Load) procedure.

What You’ll Do

The following methods are used by big data engineers to manage massive amounts of user data;

• Construction of data pipelines
• Creating and maintaining data infrastructures, including databases and large data frameworks.
• Managing data archiving
• Improving the extract, transform, and load (ETL) procedure

Skills You’ll Need

The following skills are possessed by big data engineers;

• Coding dialects like Python, SQL, Java, and C++
• Scripting and automation
• Data APIs and ETL tools
• Algorithms for machine learning.
• Options for data warehousing
•  Interpersonal and communication abilities

vi. Machine Learning Engineer

A programmer who creates self-running software that makes use of data and automates prediction models is known as a machine learning (ML) engineer. ML developers create programs that let machines operate without direct human intervention by bridging the gap between data and software.

What You’ll Do

The following duties are typically expected of ML engineers;

• Creating systems for machine learning
• Investigating and utilizing machine learning tools and algorithms
• Checking for data quality
• Working together with management to establish machine learning goals
• Utilizing stacked data sets to solve challenging challenges

Skills You Need

The following are some of the skills that machine learning engineers possess;

• Coding dialects like Python, Java, SQL, and R
• Deep learning, artificial intelligence, data analytics, and computational linguistics
• Software architecture, algorithm libraries, and machine learning frameworks
• Creating prediction models
• Algorithms, mathematics, and statistics
• Effective communication

Pro Tips

-Beginners typically begin their careers as interns in data science, junior data engineers, or junior data analysts.
-Once you have a specific amount of experience, you can pursue any of these roles.
-Additionally, these occupations call for various skill sets. For example, you must understand machine learning to work as a machine learning engineer.

3. Do startups pay data scientists more than big businesses?

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Here are the top 10 organizations that pay data scientists the most;

Company             Average Salary(in the US)

Airbnb                   US $178,030

Lyft                         US $164,372

Nvidia                   US $162,802

Apple                    US $159,575 

Google                 US $148,660

Dropbox             US $145,172

Oracle                  US $136,741

Microsoft           US $134,845

Netflix                 US $126,298

Uber                     US $123,655

Do you want to know how the type of organization affects salaries? Even with 12 years of experience, you may be a data scientist and individual contributor at a big multinational company. But with the same number of years, you could become the CTO of a startup. The amount that employers are ready to pay for their resources depends on the size and kind of the firm. The conventional belief is that remuneration increases with corporate size.

A company with 10,000 employees will undoubtedly pay more than a company with 1,000 employees, according to a University of Wisconsin article. In fact, the O’Reilly Salary Data Science Salary Report, 2016 confirms the same. This means that a startup will pay data scientists the lowest salary, followed by larger startups then mid-sized businesses, and finally, large organizations, who will pay the highest salaries. The simple explanation for this pay scale is that larger enterprises have larger finances to pay for resources than smaller businesses.

Pro Tips 

-Large businesses have established pay scales or an organized approach to salaries. Thus, it may be ineffective to continue negotiating salaries after a certain point.
-When applying to startups, which have more fluid organizational structures, data scientists must use their persuasive skills, but they must also be ready for a dynamic environment and instability.
-Startups or smaller businesses typically provide lower base salaries and greater performance-based incentives.
-Additionally, startups frequently offer better raises than large corporations since they are not constrained by standardized appraisal slabs.

Read more on the  20 Most Successful Startups of All Times

4. Does the location affect the numbers on your paycheck?

Yes is the quick response. In essence, pay packages are influenced by the place one is from and its style of living. Furthermore, several nations have labor laws establishing minimum pay for particular occupations, and workers are required to be paid according to those standards. So, a data scientist’s compensation will depend on the laws, taxes, and standard of living of the nation where he or she is employed. The top 10 nations where data scientists earn the most money are as follows;

Country                          Average Salary  

United States              US $120,000

Australia                        US $111,000

Israel                                US $88,000

Canada                            US $77,870

Netherlands                US $75,000

United Kingdom       Â£40,000  

France                            EUR 76,900

Germany                        EUR 9,470 

Italy                                  EUR 8,930

Japan                              JPY 825,000

5. Do your academic credentials affect the data scientist’s salary?

Does the university you attended affect how much money you will make? Well, in a lot of ways, sure. At the entry-level, where your only distinguishing factor in hiring is your degree, your educational background matters. In the end, the degree is not as important as your skills. There are also certain businesses that only hire graduates from Ivy League colleges, although these are extremely rare.

Frequently asked questions about data scientist salary

1. Which organizations are the best at hiring data scientists?

Microsoft Policies-by Mike Mozart-

Data scientists are hired by a variety of businesses and sectors. However, if we evaluate a company’s scope, employment opportunities, and average income offered, the following list should be taken into account;

• Intel
• Snap
• Microsoft
• Accenture
• Crayon Data
• Slack
• Lyft
• Pinterest
• Uber
• Oracle

Read more on; 55 Fastest Growing Tech Startups in 2023

2. Which sectors pay data scientists the highest incomes and have the most demand for data scientists?

The following industries, according to the well-known data science blog Galvanize, have the biggest volume of demand for data scientists;

• Information technology
• Management of companies and enterprises
• Finance and insurance
• Professional, scientific, and technical services
• Manufacturing
• Agriculture, forestry, fishing, and hunting
• Wholesale trade
• Mining, quarrying, and oil and gas extraction
• Public administration
• Utilities

The same source lists the following industries as having the highest salaries for data scientists:

• Finance & Insurance
• Professional Services
• Manufacturing

Why Do Data Scientists Get Paid So Much?

Data science occupations attract high wages due to a lack of supply (the field has approximately 20,000 unfilled positions in the United States, per US News) and the demand for their expertise. Data scientists are observing increasing demand as a result of an increase in the number of organizations that have realized in recent years the value of making data-driven decisions. Data scientists are at the vanguard of the most recent technological revolution, whether it is e-commerce companies making advances in artificial intelligence or travel companies integrating machine learning into their platforms.

Data science is a rapidly developing discipline where new tools and technologies are always emerging.