Data Scientist: Job Description, Key Skills, and Salary in 2022

A data scientist is a technical professional tasked with solving business problems with data insights by working with large volumes of structured and unstructured data, processed using their knowledge of mathematics, statistics, and computing algorithms. This article explains the role of a data scientist in detail, along with requisite skills and salary expectations in 2022. 

Data Scientist Job Description: Roles and Responsibilities

A data scientist is a technical professional tasked with solving business problems with data insights by working with large volumes of structured and unstructured data, processed using their knowledge of mathematics, statistics, and computing algorithms. 

A few years back, only a handful of people knew who data scientists were, what they do, or even aspired to be one. There was little need for them and even less recognition. Today, data scientists have climbed up the IT corporate ladder to become one of the leading, in-demand, and highly paid professionals across the globe. The demand for data scientists has massively increased, primarily due to the rise in big data technology and the role big data plays in decision making. 

With the study of data science, institutions can maximize the accumulation of big data. To that end, data scientists must source data to draw meaningful insights they communicate to the rest of the team. A data scientist must be able to find meaning in raw data, facts, and numbers and then apply it to solve real-life problems. A company or organization employs a data scientist to provide solutions and give fact-based information that aids decision-making. 

Data scientists are expected to achieve some level of expertise in programming, statistics and mathematics, artificial intelligence, machine learning, other data science skills, and communication skills. 

Role of the data scientist in a modern enterprise 

The data scientist solves real-world problems. These problems cut across every field, from healthcare and genetic studies to marketing and social media. A data scientist can use health data to determine disease trends and drug outcomes in drug studies. 

They can also predict the behavior of social media audiences to a video advert based on collected data. With this, the marketing team is correctly advised on themes they can incorporate into adverts during a particular season. Data scientists do not just analyze big data; they go a step further to make it applicable to an organization to increase revenue, reduce production costs, optimize ads and sales, and so much more. 

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10 key responsibilities of a data scientist

Understanding the responsibility of a data scientist is vital, both to every aspiring data scientist or organization considering employing one. This is because their roles can sometimes overlap with the functions of other professionals. However, these ten responsibilities are the primary objectives expected of a data scientist. 

1. Asking questions

The data scientist is first tasked with asking the right questions. Questions that identify the need for a solution to a particular problem. Every authentic research starts with a question that prompts data collection from the right source and kick-starts the discovery process. Questions can be about the target audience of a new product, consumer pain point, money spending habits of middle age in a state, etc. If the data scientist fails to do this right, every other process will be affected. 

2. Extracting data

Data extraction is another primary duty of the data scientist. Most times, there is already a pile of big data, consisting of structured and unstructured data, that the data scientist has to sieve through to get that which is essential to his cause. A data scientist may serve as the primary collector of data or may access already collected data. Data can include website traffic data, sales history for the past years, competitors’ success and failures in a particular market, surveys, IoT data, etc.  

3. Cleansing datasets

Data in its raw form is of little use; the same applies to big data. A mix of structured and unstructured data must be filtered, sorted, and processed to a more meaningful state. This is the time when data is scrubbed and cleaned up. For example, data may be missing variables, may include wrongly recorded values, need the removal of duplicates and inaccurate data, proofreading data, etc. The aim of the data scientist in data mining should be to assemble high-quality data to study trends and help decision-making.

4. Building machine learning algorithms

Data scientists are responsible for creating and developing machine learning algorithms and then training the model with cleaned data. Building a machine learning model needs diligence, experimentation, patience, and creativity. Data scientists should choose the right algorithm based on the data and purpose of the research. 

5. Powering data integration

Data integration is the process of bringing data from more than one source together to create one unified information hub for various uses. The data scientist should also integrate the organization’s data from different collection points and store them as necessary. 

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6. Conducting data analysis

Data analysis is one of the significant roles of a data scientist. It involves using statistical models and formulas to identify similar trends and recurring patterns in data. It is also the stage of verifying assumptions and answering some questions. Data analysis is done with various tools and software and requires a strong knowledge of statistics and math.

7. Keeping the organization informed

The data scientist’s job is to seek out knowledge by exploring new technologies and tools, understand which ones can be used by the organization, and inform the management about them. They also seek out new innovative insights from data for the company.

8. Collaborating with other teams in the company 

The work of the data scientist should not be done in isolation. You should keep other units, such as the business team, the marketing team, the product development team, and the IT team should be kept abreast of new findings. They also have to keep the data scientists in the loop of the company’s activities. 

9. Creating data visualizations

Data visualization is a crucial role that the data scientist must be adept at. Results from data analysis and processing using machine learning have to be presented to data-illiterate members of the organization who will then approve the new idea or implement the insight from studies. If it is not shown in a format that is easy to understand, there is a possibility of miscommunication. Data visualization techniques include using graphs, tables, charts, etc.

10. Offering solutions to solve business problems

The primary responsibility of the data scientist, irrespective of the enterprise they are located in, is to find answers to the company’s problems using data. 

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Essential Skill Requirements for Data Scientists in 2022

A good data scientist has the right combination of hard and soft skills needed for the job. This includes hard skills such as:

1. Statistics and mathematics 

For a data scientist, statistics is one of the core skills needed for data studies. Statistics is the root of machine learning algorithms and is heavily applied to programming and research. Statistics has to do with data collection, analysis, making conclusions, and visualization of results. In the same way, basic mathematical skill is needed for the data scientist to apply statistical formulas, among other uses. 

2. Knowledge of database 

Data, both structured data and big data are stored in databases. Therefore, data scientists cannot function without knowing how to access, manipulate, query, and obtain results from a database. This requires the data scientists to learn SQL and NoSQL to be fully proficient in working with relational or non-relational databases, as the case may be. A data scientist should be familiar with databases such as MySQL, Oracle, Microsoft SQL, Cassandra, and MongoDB.

3. Programming language and tools

A data scientist primarily communicated with computers and then with humans. Knowledge of programming language is integral to his ability to do this. There are numerous programming languages, and a data scientist is not expected to know all of them. Programming languages like R, Python, SQL p, SAS, and Scala are commonly used by data scientists, and they should be able to master one out of the three. 

Python is an open-source, widely used programming language with libraries like NumPy and SciPy used by data scientists. R programming is free software that suits several machine learning models and is used for statistics and graphics. Another example of a programming language that can use statistics to analyze data is SAS. 

4. Machine learning 

An experienced data scientist should be able to understand and apply machine learning to automate some parts of data processing. Machine learning as a new technology automatically analyzes large chunks of data and solves problems that might otherwise be overwhelming to scientists. 

Using it, computers can solve problems and, better still, learn from experience to improve at a particular task without further human input. Machine learning is a subset of artificial intelligence that has replaced several old statistical methods and provides greater accuracy. 

5. Data wrangling 

Data wrangling is the process of cleaning data, eliminating errors, manipulating, combining data sets, and organizing data for easy analysis. Data wrangling is among the essential skills a data scientist should possess. Data wrangling involves the following steps which are;

• Data discovery: This helps the scientist understand the data and techniques to use in the analysis. 
• Structuring: This involves organizing and restructuring data in consumable forms.
• Data cleaning: Data must be cleaned, formatted, and error-free.
• Data enrichment: This involves adding more data to the current data set if necessary.
• Data validation: This is done to prove the consistency and quality of the cleaned data. After this, you can use the data for analysis. 

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6. Big data

Big data is a pool of structured, semi-structured, and unstructured data generated in huge quantities in a very short time. It describes complex data that one cannot easily analyze conventionally. A data scientist should have a working knowledge of big data and how to use big data tools. Some of these tools include Apache Spark, Hadoop, etc. 

In addition to these hard skills, a data scientist must display some vital soft skills. They include:

7. Communication skills 

A data scientist does not exist in a bubble and should be able to express his findings appropriately. They should be able to link their scientific background with the day-to-day business world. Data scientists may work with technical professionals like data analysts and data engineers. 

While this might be easier, the data scientists will also have to communicate effectively with the manager, the marketing team, the content creation team, the office staff, employees in other organizations, and so on. Communication is a vital skill that one should find in every data scientist. Effective communication can also increase the level of data literacy among co-workers. A data scientist is a team player that needs to be able to participate in the exchange of information among members of the team. 

2. Storytelling 

Storytelling can be seen as an expressive way of passing information across. Data scientists must know how to communicate and compellingly share their results. For instance, during a presentation by the data scientist, the audience should be able to picture the outcome and the market reaction of a pending decision. Storytelling is the ability of the data scientist to use data visualization techniques to build a robust data narrative for the audience. 

4. Adaptability 

The world is rapidly advancing, and so are the skills required to keep up with new technologies. Just as there were hardly any data scientists a few decades back, there may be changes in the job description or roles of a data scientist in some years’ time. 

Technological innovation keeps accelerating, and data scientists must keep up with the times. This boils down to being aware of new software, breakthroughs in machine learning, change in business trends, better ways to collect data, and so on. An organization that wants to be at the top of its niche must be abreast of changes in all areas. This makes adaptability a non-negotiable soft skill for a data scientist. 

5. Curiosity 

A data scientist should be curious. The job requires not just providing answers but to ask questions also. It takes great curiosity to search for a better way of doing things and get the best possible insight from data. A curious data scientist is bound to discover more from a given data set about a particular business challenge than one who accepts all information at face value.  

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Data Scientist Salary in 2022

The demand for data scientists has risen strongly, and so has the yearly remuneration. Data scientists are among the most sought-after information and technology professionals. There is still a wide gap in the demand and supply of highly skilled data scientists that can solve real business challenges. With a suitable skill set, degrees (such as certifications through SQL courses), and proof of experience, you can set yourself apart and expect exceptional pay at the end of the year. 

The expected salary differs across different states and major cities. It depends on the number of companies placing a demand on the role in that area. The data scientist’s salary can also be affected by the level of the data scientist, years of experience, and the addition of any managerial or administrative role. Different countries may also offer differing pay to data scientists. 

In the United States, the base salary for a data scientist ranges from $69,000 to $136,000 as per PayScale data (last updated on July 15, 2022). On average, you will find the majority of the data scientists earning $98,000 without any additional pay. 

The data scientist can also expect a yearly bonus of $3,000 to $20,000, depending on their company. Profit sharing could amount to an extra $999 to up to $25,000. If the company pays a commission based on specific business outcomes, this will probably fall within $2,000. With all this added pay, the data scientist can go home with up to $146,000 per year.  

Data scientist salary expectations based on level of experience 

As earlier mentioned, data scientists can earn different pay based on years of experience and are expected to make more as they advance in their careers. The salaries listed here comprise base salary tips, bonuses, and commission. 

• At the entry level, a data scientist with not up to one year of working experience can expect to earn a total of $85,751. 
• A data scientist with more than one year but less than five years of experience early in his career can earn up to $96,488.
• Data scientists with 5-9 years of experience at a middle career level generally receive a total salary of about  $110,852  
• Experienced data scientists with over ten years of experience are paid slightly above $120 000, and those with over 20 years of experience have an average take-home salary of $136,229.

There is also some disparity in the salary of data scientists based on where they work. For example, San Francisco and California-based data scientists are among the highest paid. At the same time, places like Chicago, Atlanta and Washington DC offer some of the least paid data scientist jobs in the US. Data scientists are usually highly satisfied employees, with 90% boasting medical benefits. 

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Takeaway 

Data scientist is among the most in-demand jobs of our decade. According to Glassdoor’s 2022’s research, it is the no.3 occupation in the U.S., with a job growth surge of 480% in recent years. For technical professionals or those with general science, technology, engineering, and mathematics (STEM) background, it is among the top jobs to aspire for in 2022 and beyond. 

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