What does a data scientist do for businesses? (With careers)

By Indeed Editorial Team

Updated 15 September 2022 | Published 3 January 2022

Updated 15 September 2022

Published 3 January 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data scientists create value from data by turning large groups of text and numbers into useful information. A data scientist's job is to identify patterns in large sets of data and use their findings to make predictions about future outcomes. Data science has many applications across different industries, including finance, marketing, healthcare and manufacturing. In this article, we answer the question 'what does a data scientist do?', discuss how they aid human resources, businesses and marketing efforts and list the job requirements for the role and other related careers.

What does a data scientist do?

If you're good with numbers, you may be curious to know 'what does a data scientist do?'. Data scientists primarily use their analytical and computational skills to help businesses and other organisations make better decisions. They can help identify trends, patterns, correlations, causes and effects in data sets. Data scientists use different tools to gather data sets and then analyse them. They also use these tools to predict outcomes based on their findings to create applications, optimise business models or make better decisions for customers.

Large corporations or government agencies need data scientists to process large amounts of data. Their primary concern is how to effectively understand, collect, and visualise data to help solve complex problems. Data science is one of the most important fields for any company in today's market, as it allows them to generate ideas on how a company may gain a competitive advantage.

Related: Data analyst vs data scientist

How data scientists aid marketing by social media

Data scientists use their knowledge and expertise to create successful ad campaigns. Today's data scientists are experts in both marketing and social media, allowing them to find new and creative ways to leverage the sorts of platforms people use every day. In marketing, data scientists are revolutionising the way companies use social media to promote their products. They use artificial intelligence, machine learning, and natural language processing to collect information from disparate sources, such as social media posts, in a non-intrusive and scalable way.

How data scientists help human resources and people connect

Using data science in human resources is becoming more prevalent. Data scientists are helping companies find the best talent by looking into the social media profiles and websites of candidates. Data scientists are also beginning to develop algorithms to help people find their passion or fit into a specific working culture. This occurs by defining what makes a person happy and then analysing all available data with machine learning algorithms to find company cultures with similar tastes and interests.

Related: How much does a data analyst make? (With qualifications)

How data scientists help businesses today

In the past, companies used data scientists to develop tools for large-scale data analysis and predictive modelling. Data scientists turned several different business processes that previously required a lot of manual work into automated processes. Today, you can assist with customer support, for example, in your role as a data scientist by providing:

  • insights into how their customers think and behave

  • higher sales by better understanding customer decision-making behaviours

  • better marketing by understanding how brands can best reach their target audience

  • more lead generation through better understanding the habits of existing customers and potential leads.

  • better customer engagement and retention through personalisation and targeting

  • new products by creating predictive business analytics that identify future customer needs

  • applications that use artificial intelligence to automate tasks like customer service, sales support or billing

  • machine-learning algorithms to create personalised content for each audience

The job outlook for a data scientist

Data scientist jobs are in high demand because of the tremendous opportunities they offer in terms of career growth and financial compensation. As a data scientist, you typically involve yourself with analytical tasks such as finding patterns in numbers. Some tasks may involve improving business models through predictive modelling or testing statistical hypotheses with research methods, such as controlled experimentation or surveys. Because a data scientist can have many different duties, some are their own separate careers. All of the careers below make use of data science in some form:

1. Data scientist

National average salary: £50,417 per year

Primary duties: Data scientists analyse data to understand what happened in the past, what's happening now, and how to predict trends. They use statistical research methods to explore data sets. The role differs from that of a data analyst, as this position involves more statistics and maths.

Related: How to become a data scientist in 4 steps

2. Machine learning engineer

National average salary: £56,811 per year

Primary duties: Machine learning engineers are integral to the evolution of artificial intelligence. They design and develop algorithms that provide machines with the ability to learn from data. The machine learning engineer's job is to find patterns in extensive sets of data, which could be images or videos. One of their primary tasks is to make sense of what a computer sees for the first time, using algorithms and human-created rules.

Related: How to become a machine learning engineer in 4 steps

3. Machine learning scientist

National average salary: £35,097 per year

Primary duties: Machine learning scientists develop algorithms to solve problems in a variety of domains. This can range from modelling the spread of diseases to optimising the next round of stock trades. Machine learning scientists are occasionally referred to as research engineers or research scientists.

Related: 5 common jobs in machine learning (and how to find them)

4. Applications architect

National average salary: £75,723 per year

Primary duties: Applications architects design, build and manage the application architecture for a business or enterprise. This includes the design, development, testing, deployment and support of hardware and software applications. They work with software developers to ensure that systems are scalable. They determine the best application architecture for the needs of the organisation and for managing technical risks.

5. Enterprise architect

National average salary: £79,811 per year

Primary duties: The enterprise architect plans, designs and develops a company's IT strategy. They provide leadership and guidance on all aspects of IT. These individuals work closely with the CEO and other executives on long-term strategic planning and vision.

Related: What is enterprise architecture? (With definition and goals)

6. Data architect

National average salary: £48,603 per year

Primary duties: The data architect designs, maintains and optimises the overall quality of the data within an organisation. They ensure that this data is easily accessible by all users across various departments and through external sources. Data architects are also responsible for building systems to manage data.

Related: What is a data architect? (Job definition and roles)

7. Infrastructure architect

National average salary: £75,723 per year

Primary duties: Infrastructure architects regularly update business systems so that they can continue to work optimally with the development of new technologies and system requirements. The role requires a vast amount of technical skills and the ability to stay up to date with technological developments. Implementing or updating systems can be disruptive, so infrastructure architects try to make adjustments in the most efficient ways possible.

8. Data engineer

National average salary: £59,861 per year

Primary duties: Data engineers are responsible for the gathering and storing of data. They also analyse and extract information from raw data to create reports, visualisations, and dashboards to use as effective tools for decision-making. They extract data from a variety of sources and turn it into information for use within a company. Along with a variety of other tasks, they build and maintain data pipelines that create an interconnected data ecosystem within an organisation, making information available for its data scientists.

Related: Data scientist vs. data engineer (differences and FAQs)

9. Business intelligence analyst

National average salary: £42,321 per year

Primary duties: Business intelligence is the process of gathering data from various sources and making it accessible to a business. A business intelligence analyst helps with decision-making and planning. The business intelligence unit provides insights into how the company can increase its revenue and market share by using analytics and predictive modelling.

Related: What is business intelligence? (Definition, tools and FAQs)

10. Statistician

National average salary: £40,277 per year

Primary duties: Statisticians are individuals who use mathematics and statistical models to help organisations make sound decisions. Many data scientists work predominantly with statistics, especially those in government positions where much of the data is about population trends. Once they acquire data, statisticians may make recommendations for how organisations can act upon the information.

11. Data analyst

National average salary: £34,047 per year

Primary duties: Data analysts are the key driving force behind the success of any business. They transform data into insights that help a business gain a competitive advantage. Their responsibilities include data collection, analysis, and reporting activities. These professionals use data to make informed decisions and use analytics to help them make better business decisions.

Related: How to become a data analyst

Requirements for becoming a data scientist

To become a data scientist, it's good to have a desire to work with large data sets. If you understand statistics and programming languages like Python or R they can also help you with your research process. Data scientists typically have degrees in fields such as engineering, business, computer science, social science and mathematics. You can hone these skills through related certificates or by gaining experience working with similar technologies in other industries like marketing or creative fields. The key components of becoming a good data scientist are:

  • having excellent coding skills

  • learning statistics

  • understanding machine learning

  • mastering big data and distributed computing

  • knowing how to write code scripts in R or Python

Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries‌ ‌may‌ ‌‌vary‌‌ ‌depending‌ ‌on‌ ‌the‌ ‌hiring‌ ‌organisation‌ ‌and‌ ‌a‌ ‌candidate's‌ ‌experience,‌ ‌academic‌ background‌ ‌and‌ ‌location.‌

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