How Much Does it Cost to Hire a Data Scientist in 2022

Data is a highly valuable asset for any modern business. It helps businesses understand and improve their processes, thereby saving time and money. But data is meaningless until its conversion into usable information. Data science is the process of mining large sets of structured and unstructured data and determining covert patterns to obtain actionable insights. 

There are various reasons why data science is so important for businesses. It allows them to measure, track and record a variety of performance metrics for facilitating enterprise-wide improved decision-making.

Businesses can efficiently analyze trends to make crucial decisions to better engage customers, improve company performance, and expand profitability. 

Data science models make use of existing data and can affect several actions. As a result, businesses can develop the path to gain the best outcomes. For example, data science helps businesses define target audiences by integrating existing data with other sourced data points for generating useful insights. Used right, data science can also help recruiters hire the right candidates for businesses. 

Today, increasingly more businesses are opening their doors to big data and working to unlock its power. To do this effectively, they are hiring skilled and experienced data scientists. 

Who Are Data Scientists and What Do They Do?

Data scientists are a fairly new but growing group of professionals that are highly in demand today. This demand is the result of the sudden need to find skilled people who can work with data and help discover and, ultimately, empower businesses to make strong, data-driven decisions. 

Data scientists use knowledge about statistics, computer science, and mathematics to analyze, interpret and store different types and volumes of data. 

Some of the key responsibilities of data scientists include: 

• Identifying suitable data sources for the needs of the businesses they are working with
• Collecting a variety of data sets, including structured and unstructured data
• Organizing large volumes of data into usable formats
• Building a variety of predictive models
• Building useful and effective machine learning algorithms
• Enhancing the collection process of data 
• Processing and verifying data
• Analyzing data for patterns and trends and finding answers to certain questions
• Setting up data infrastructures
• Developing, implementing, and maintaining databases
• Assessing the quality of data and removing some sets of data or cleaning data sets
• Generating insights from data sets and identifying patterns
• Preparing reports for stakeholders
• Creating unique visualizations of data

 

To complete these tasks, data scientists require a range of specialized skills, some of these include

• Computer science

Applying the principles of database systems, artificial intelligence, human and computer interaction, software engineering, and numerical analysis.

• Statistical analysis

Identifying patterns in data, including having a good sense of detecting patterns and anomalies.

• Machine learning

Implementing statistical models and algorithms to enable computers to automatically learn from different data sets.

• Programming

Writing programs for computers and analyzing large sets of data to find answers to complex problems. Data scientists usually need to be proficient in writing code working with languages such as Python, Java, SQL and he need to know some caching functions.

• Data storytelling 

Communicating actionable insights by using data.

Some of the soft skills data scientists need to include

• Analytical thinking 

Finding effective analytical solutions to abstract business issues.

• Communication

Effective and efficient written and verbal skills with all stakeholders.

• Business intuition

Connecting with stakeholders to understand the problems they want to solve.

• Critical thinking

Objectively analyzing facts before finding conclusions to different problems.

Source: https://mobilunity.com/blog/hire-data-scientists/ 

Currently, data science is considered one of the most in-demand careers available. Businesses across all major industries have data scientists and data development service needs to help them obtain useful insights from big data. There is a big increase in demand for highly skilled data science experts who can manage the business world. 

Benefits of Working With a Data Scientist

Data science involves the using a variety of tools, data mining and statistical techniques, as well as algorithms and machine learning methods to determine different patterns, trends and insights from sets of raw data. Data scientists work to extract meaning from this data, uncover valuable insights and identify unique opportunities to improve business decision-making. But what are the exact benefits of working with the best data scientists for businesses? Let’s explore: 

Helping businesses define clear goals

Data science plays a highly important role in using data and analytics to enhance the customer experience in all the different parts of business operations and processes. With the help of a data scientist professional, a business can study and explore its raw data and create a division that will improve its performance, as well as engagements with customers and, ultimately, profitability. Data Science offers information about consumer patterns and behaviors to help businesses develop essential marketing campaigns and highly targeted advertising to increase sales.

Identifying a target audience

Most businesses have at least one source of customer-centered data that is stored. This information is highly useful in identifying demographics and, as a result, finding potential customers. Data scientists can help with the identification of key groups of customers by using a thorough analysis of data. This also helps businesses to personalize offerings for customer groups and improve profit margins.

Gaining a better understanding of customers

Customer behaviors are constantly changing over time, and it can be fairly difficult to monitor these changes without the help of a data scientist. For example, a business can constantly review its consumers’ behavior during website searches and revamp its algorithms to offer more customized results. As a result, sales may increase. It’s this type of glimpse into the actions of a business’ customer base that data scientists can uncover to improve business models.

Making better decisions 

A skilled and experienced data scientist can act as a trusted advisor to a business’s upper management. They can communicate and demonstrate the value of the data that they collect and analyze to facilitate an improved decision-making process.

Recruiting the right talent

For some businesses, data science has been used to actively change the recruitment process. With the increasing demand in the job market, recruiting managers have a very difficult time sourcing candidates and trying to find the perfect workers for businesses. Recruitment data has been proven to be challenging to analyze. Recruiters typically have to read through several resumes a day, to find the right candidates. Data science makes recruitment much easier by mining a large amount of data online. Data scientists work their way through different platforms and databases to find suitable candidates for businesses’ needs. When used right, data science can help recruiters expedite data-driven tests throughout the selection process.

Improving forecasting

Mining data through the use of machine learning and other sources have been implemented for some time to forecast results in the future, and many data scientist specialists have skills in both these areas. For example, a bakery business could analyze increases in consumer visits over several years to write better employee schedules.

Assisting with business expansion

Working with data scientists, businesses can uncover new markets that would potentially be interested in their products or services. A specific advertising campaign of a business may be solid, but a data scientist can review it and identify the type of new customers acquired from a specific initiative so the business can improve future campaigns. Data science can also be implemented to discover new trends or determine particular inventory items that can have a more immediate influence on revenues.

Improving data security

In today’s data-centric world, businesses require procedures to manage individual data for both legal and ethical reasons. By using data science, businesses can increase security and protect critical and sensitive information. For example, some financial institutions use machine-learning algorithms to find frauds compared to typical financial activities. These algorithms are designed to catch fraud more accurately and faster than humans because of the large amount of daily data. Businesses in other industries can also use algorithms to protect sensitive information from staff and customers through encryption. 

The Most Popular Models for Hiring Data Scientists 

Finding skilled and experienced data scientists can be a challenge. If a business has current staff members with good IT acumen, they may be able to grasp data science software programs over time. However, for faster results, it is recommended to rather source trained data scientists specialists who understand the ins and outs of the trade right from the start. Let’s now explore some of the most effective ways businesses can source data science specialists:  

Hiring in-house 

For some businesses, it can be useful to hire a data scientist specialist as part of an in-house team. This hiring is specifically useful for businesses that want to have a long working relationship with data scientists and have the right resources in place, including hardware, software, and office space.

Using an outsourcing company 

There are many benefits of hiring a data scientist specialist from an outsourcing provider, including having access to a large talent pool, being able to scale whenever required, avoiding HR tasks, and saving time and money. 

Hiring a freelancer 

For a complete part-time experience, businesses can also hire data scientist candidates from freelancing platforms. This hiring model is ideal for businesses that want to work with a professional for a limited time. There are some drawbacks to using this hiring model, including difficulty proving skills and experience.

When interested in hiring professionals to work with data, it is important to know that there are different careers in data science, including:

Data scientists

These professionals specialize in designing data modeling processes to develop algorithms and highly predictive models and conduct custom analyses. 

Data analysts

These experts work to manipulate big sets of data and use them to identify patterns and trends and reach significant conclusions to help make strategic business decisions.

Data engineers

These engineers work to clean and organize data from various sources and transfer it to different data warehouses. They often work with Salesforce outsourcing solutions sourced from Salesforce consulting companies in Ukraine to import and export, as well as delete unlimited amounts of data.

Business intelligence specialists

These professionals specialize in identifying trends in data sets to improve operations and overall profitability for businesses. 

Data architects

They work to design and manage a business’s data architecture. This involves reviewing and analyzing the data infrastructure of a business, planning future databases, and implementing solutions to store and manage data.

The Cost of Hiring a Data Scientist

Data scientists are growing in demand in a wide range of industries, ranging from science and research to healthcare. Businesses are continuously using data and data scientists to help them analyze and make sense of these large sets of data. While many businesses have entire teams of data scientist professionals working in-house, not all companies can afford to hire full-time data scientists. This is why it’s important to consider different hiring models. 

However, it is always a good idea for any business to do some research to find out the exact costs of data scientists for hire. 

So, how much does it cost to hire a data scientist? Let’s take a closer look:  

In the United States, the average salary of a data scientist is around $117,345 per year. The salary of a data scientist in the United Kingdom can be anything upwards of $75,000, even exceeding $122,000. In some other Eastern European and Asian countries, the average salaries are typically lower. For example, the average annual salary for a data scientist in Ukraine can be as low as $15,309.88.

For freelance data scientist professionals, the hourly charge can be anything in the range of $35 to $200 per hour, and the fixed fee per project can be around $2000.

Source: https://mobilunity.com/blog/hire-data-scientists/ 

While these figures are good indications of the cost of hiring qualified data scientists, it is also important to realize that the rates may change according to years of experience and skill. Some senior data scientist experts may have a much higher salary compared to one that is just starting. For businesses that want to hire these professionals and budget correctly, it is very important to do thorough research and get up-to-date figures.

Other Specialists Working With Data Scientists

Setting up a data scientist team can be a complex process. This is because there are so many different roles and responsibilities to consider. This is why many businesses choose to outsource the roles within these teams. Let’s now take a look at some of the most important specialists who work with data scientists and who are most regularly outsourced as part of data scientists’ teams: 

Chief data officer 

A chief data officer has the role of overseeing a range of data-related tasks to ensure a business is getting the most from its most valuable asset. The chief data officer is a senior executive who is responsible for the utilization of data across the company.

Data Analyst

A data analyst has the role of collecting proper data and interpreting activities. The analyst ensures that the collected data is relevant and complete while also analyzing the analytics results. Some companies also need data analysts to have certain visualization skills to convert certain sets of data into tangible insights through valuable graphics.

Data architect

This role is important for working with large amounts of data. A data architect works closely with data scientists to define the policies, procedures, and models to be used to collect, organize, store and access information. 

Data engineer

Data engineers have the roles of implementing, testing, and maintaining infrastructural components that data architects typically design. Realistically, the role of the data engineer and the role of a data architect can be combined in one skilled person, because the set of skills for each position is so close.

Machine learning engineer 

Machine learning engineers have the task of designing and creating AI algorithms capable of learning and creating predictions that define machine learning. These engineers communicate with data scientists, data analysts, engineers, and architects.

It’s important to note that data science teams can look for different things for various businesses. For example, smaller businesses and startups may only require a data scientist to work on their data sets, while bigger companies may need an entire team. It depends on unique needs, the size of projects, and budgets. 

The Bottom Line 

When used right, data science can add a lot of value to any business. From gaining statistics and insights across different workflows and hiring the right candidates, to helping staff make better decisions, data science is highly valuable to any business in any industry.

However, businesses need to hire the right candidates to source and manage their data. This is where hiring skilled and experienced data scientists come in. Data scientists help businesses correctly interpret and manage data, while solving complex problems using their expertise in a variety of data niches. This can help businesses position themselves to enhance customer experiences and improve their in-house teams and processes. 

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