Data Scientist Job Profile

Part mathematician, part computer scientist, and part trend-spotter, data scientists are employed to convert raw data into meaningful information that organisations can use to improve their businesses.

In the current big data climate, companies use increasingly large data sets during their everyday operations. From predicting consumer habits to tackling plastic pollution, data scientists identify patterns and use empirical methods to solve the problems businesses face. 

If you’re thinking about becoming a data scientist, we’ll tell you everything you need to know about the role and how it fits into a larger career in data science.

What is a data scientist? 

A data scientist is employed to extract, analyse, and interpret large amounts of data from a range of sources, including artificial intelligence, machine learning, statistical tools, and more, to make it accessible to businesses. They then present their results in a concise, digestible manner. Data scientists are coveted across several sectors, as companies seek to boost revenue by unearthing data-driven insights. 

Data scientists will generally work in an office, but there may be times, particularly on short-term projects, where working outside of core office hours or at weekends is necessary. Some organisations also offer remote and flexible working opportunities. 

Responsibilities

Your responsibilities as a data scientist will vary depending on where you’re employed and what level of experience you have. Despite this, almost all data scientists will have a broad role that involves some or all of the following responsibilities:

• Working closely with your business to identify issues and use data to propose appropriate solutions for effective decision making

• Building algorithms and design experiments to merge, manage, interrogate, and extract data – you’ll then provide this information to colleagues and customers in a concise, engaging manner

• Testing data mining models and selecting the most appropriate for use on a project

• Conducting research that will serve as a platform for prototypes and proof of concepts

• Using machine learning tools and statistical techniques to produce effective solutions to complex problems.

Data scientist qualifications

While there are no set qualifications that are absolutely required to become a data scientist, you will stand a better chance with a degree in a computer science, mathematical, or science-based subject. Candidates will be expected to know some programming languages, such as Python or Java, and show strong database design or coding skills.

Because there are no strict learning paths to becoming a data scientist, you have several options to consider to improve your chances of beginning a career in data science.

Level 4 Data Analyst Apprenticeship  

This apprenticeship introduces students to a range of essential skills, from data analysis tools to programming software such as Python and R. During your data analyst apprenticeship, you’ll develop skills across the board that will enable you to generate insights to inform better decision making, which is crucial to any modern business.

Level 6 BSc Digital and Technology Solutions Professional Degree Apprenticeship (DTS)

Ideal for new and existing employees in digital and technology roles, this programme offers a solid foundation for professionals in software engineer, data science, cyber analysis roles, and IT consultancy.

The degree apprenticeship is available in four core specialisms: Software Engineer, Cyber Security, Data Analyst, and IT consultant – therefore on this course, you will select the data analyst pathway. The specialist section of the course takes place in the final year, when you will gain a solid grounding of the detailed technical knowledge required to work as a data analyst.

Level 7 MSc Applied Data Analytics Apprenticeship

The MSc Applied Data Analytics Apprenticeship is aimed at the future stars of data analysis who will tackle real world problems using data science. Upon completion, graduates will be able to  leverage the power of both big data and domain knowledge to generate insights and devise strategies for a range of industry leading organisations.

Data science graduate training schemes

Some employers offer data science graduate training schemes, which typically take two years to complete. Many of these schemes are open to graduates from any discipline, while others specify the degree subjects they will accept.

Skills

SQL

SQL, or Structured Query Language, is the ubiquitous industry-standard database language. Regarded by some as a graduated version of Microsoft Excel due to its smooth management of substantial data sets, most modern organisations require an SQL specialist.

Critical Thinking

Data analysts uncover and synthesise connections that are not always clear, so the ability to think critically is essential.

Machine Learning

With AI and predictive analytics at the forefront of data science, a comprehensive understanding of machine learning is a key component of the data analyst’s toolkit. To excel in this area, consider boosting your statistical programming skills.

Presentation Skills

Data analysts are required to generate insights and present their findings in a clear and engaging way to their colleagues or clients. Telling a compelling story with data is also a key aspect of the role, and analysts often employ eye-catching, high-quality graphs and charts to convey their findings effectively.

Salary and benefits

Salaries for junior data scientists tend to start at around £25,000 to £30,000, rising to £40,000 depending on your experience. With a few years of experience, you can expect to earn between £40,000 and £60,000. Chief data scientists can earn upwards of £60,000, reaching more than £100,000 in some cases. The benefits for data scientists vary by organisation, but are likely to include a company pension scheme, flexible or remote working, performance bonuses and private medical insurance.

Career path

Work experience

Data science internships are available at several major employers, particularly in the finance, retail, and travel industries. You could also approach small to medium-sized enterprises for internship or shadowing opportunities. Most internships or placements are advertised in the autumn. Bear in mind that with smaller organisations, you may need to make targeted speculative applications.

Is work experience essential?

Many organisations require their entry-level data scientists to have demonstrable work experience before applying. But don’t worry if you haven’t, as any self-directed learning in data analysis or programming will highlight your commitment and enthusiasm for the role.

Data science competitions

Data science competitions represent another option for aspiring data scientists. Used by employers to spot new talent, organisations such as Topcoder and Kaggle host events, while the government backed Data Science Challenge aims to ensure that science and technology contribute to the UK’s defence and security.

Continuing professional development

While there is no official accreditation for working in data science, you will build on your analytical and technical skills as you gain experience in the role. Some major employers offer two-year data science graduate training schemes. In contrast, others provide additional training in their operating procedures or encourage you to attend sector-specific events to help your understanding of potential issues, new developments, or emerging trends.

Find out more about our data and tech programmes here.