A typical screen of what Data Scientists might look at while programming to work with data.Credit: Photo by Jorge Jesus from Pexels
A typical screen of what Data Scientists might look at while programming to work with data.

Job Profile

Data Scientist

Data Scientist is such a broad term that no two job specifications for this role may look the same. There are a few different categories this position falls under, three of which will be covered here: Research-based; Business-focussed and Development-orientated. In either case, the job of a Data Scientist is to collect, analyse and interpret large amounts of data in line with the project they are working on.


A research-based data scientist will start working on a project by reading research papers to better understand the data they are working with. Then, they will use either a machine learning model, a simple statistical model or data exploration, in order to make predictions according to the data. Eventually, this tested model is employed for larger use by the company.

The aim is to produce a tried and tested model which succeeds at its objective. Research-based data scientists were involved in the building and testing of models, which ultimately led to the capture of the Pōwehi black hole image of 2019.


In Development, data scientists work with the data in applications, software, or other technology tools. Their work involves writing and debugging code (fixing code that has errors to make it run correctly), attending meetings to provide updates on their work and take note of additional tasks, as well as designing algorithms.

Statistics and programming languages such as Python or R are in use throughout their work. They also use their communication skills when updating colleagues on their work and problem-solving together. The goal is to make sure the application or systems run smoothly.


In the business domain, data scientists drive business growth and efficiency through transforming data into actionable insights a company uses to enable executive decisions that will benefit the business.

They must schedule their day according to priority tasks, so need to be very organised. In order to effectively translate their technical work into non-technical jargon for other members of the business, they will need to communicate effectively and confidently deliver presentations discussing their work.


Starting salaries range from £25000 - £30000 for junior data scientists, reaching £40000 according to experience. While the average salary is £51840, over the years salaries can reach £60000, with lead data scientists earning up to £100000.


  • Coding – Python, R, SQL, C, Java, CSS.
  • Organisation
  • Analytical skills
  • Problem-solving skills
  • Attention to detail
  • Teamwork
  • Communication
  • Presentation delivery
  • Self-motivation
  • Resilience
  • Ability to work under pressure
  • Capable of working to tight deadlines
  • Knowledge of database & analysis software.

Routes in

  • A relevant degree in physics, statistics, mathematics, engineering, computer science or data science, with proven coding and modelling experience in programming languages such as Python, R, SQL or Java.
  • A relevant Masters or PhD in Business Analytics, Data Science, Data Analytics or Big Data.
  • An Apprenticeship, such as those offered by the Defence, Science & Technology Laboratory. First year salaries range from £14358 to £27010 depending on the level of the apprenticeship. Salaries rise yearly.
  • Graduate trainee schemes in Data Science, which are generally completed within 2 years. They tend to start in September annually, generally recruiting from September to February, though some applications may close later in the year, depending on the graduate trainee scheme. This is a great way to be remunerated (usually starting at £30000) and learn on the job. Successful candidates might have the opportunity to stay working at the company after the graduate scheme is over as a full-time member of staff. Entry requirements for graduate trainee schemes are usually a 2:1 or a 2:2 in a relevant degree.


Mélissa AZOMBO

Mélissa studied BSc Observational Astronomy at University of South Wales, PGDip Science Communication from Bristol UWE & graduated with MSc Astrophysics from Queen Mary University London. She currently writes articles while persuing a career in astronomy through data analysis.

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