Data Scientist Salaries in the UK and the Skills That Raise Them

The median advertised salary for a data scientist in the UK is £75,000, rising to £80,000 in London. That compares with a UK IT industry median of £57,500. Data scientist pay has risen 7.14% over the past year, even as much of the wider technology jobs market has been broadly flat. But the median is only part of the story. The bottom 10% of advertised data scientist roles pay £50,000. The top 10% pay £100,000 or more. The job title may be the same, but the salary can be twice as high. What explains the difference isn’t simply time served. It’s the combination of technical, commercial and deployment skills employers are prepared to pay for. This guide looks at what UK data scientists earn at different career stages, then examines the skills associated with higher salaries. If you’re considering moving into the field, it also explains where a programme such as the Cambridge PACE Data Science with Machine Learning and AI Career Accelerator fits.

What does a data scientist earn in the UK in 2026?

The median advertised UK data scientist salary is £75,000 as of August 2026, rising to £80,000 in London. Junior roles typically start at around £25,000 to £30,000. Senior data scientists earn from roughly £60,000, while the top 10% of advertised roles pay £100,000 or more.

UK data scientist salary at a glance (advertised permanent roles, six months to 26 August 2026)

 

Measure	UK	London
Median	£75,000	£80,000
Year-on-year change	+7.14%	+1.59%
10th percentile	£50,000	Not published
25th percentile	£60,000	Not published
75th percentile	£95,000	Not published
90th percentile	£100,000	Not published
Permanent job ads	591	246
Share of all UK permanent IT vacancies	0.54%	Not published

Source: IT Jobs Watch, Data Scientist UK and IT Jobs Watch, Data Scientist London, six months to 26 August 2026. There’s an important distinction between advertised and reported salaries. Advertised medians show what employers are offering for vacancies now. That matters if you’re deciding whether to retrain, change jobs or negotiate a new package. Reported salary averages from sources such as Glassdoor and Payscale measure what the existing workforce earns. These figures can run lower because they include employees hired several years ago on older salary packages. For that reason, this guide focuses primarily on advertised salaries. For a broader comparison, see the highest paying digital jobs right now.

Data scientist salary UK by career stage

The £75,000 advertised median is weighted towards experienced hires. Someone entering the profession shouldn’t expect to start there. The more useful question is how salaries change as responsibility, technical depth and commercial ownership increase.

Entry-level and junior data scientist: £25,000 to £30,000

Junior data scientists typically start on £25,000 to £30,000, with salaries rising towards £40,000 as experience builds (Prospects). This is also the most competitive part of the market. At junior level, employers have relatively little professional evidence with which to distinguish one candidate from another. That makes portfolios, practical projects and evidence of applied work particularly important. Career changers can have an advantage here. Someone moving from finance, actuarial science, engineering, healthcare or academic research may already have commercial or analytical judgement that a new graduate still needs to develop. If that sounds familiar, read why now is the best time to pursue a career in data.

Mid-level data scientist: £40,000 to £60,000

After a few years in the field, data scientists typically earn £40,000 to £60,000 (Prospects). The shift at this stage is less about learning another tool and more about ownership. Junior practitioners are often given the problem and asked to execute. Mid-level data scientists increasingly decide how a problem should be approached, which method is appropriate and what trade-offs matter. Why, then, does the advertised UK median sit at £75,000? Because the sources measure different things. Prospects describes typical salary bands by career stage, while IT Jobs Watch aggregates advertised vacancies across the market. Job advertisements tend to be weighted towards experienced hires.

Senior data scientist: £60,000 upwards

Senior data scientists typically earn from £60,000, while the 75th percentile of advertised UK data scientist roles sits at £95,000 (IT Jobs Watch). At this level, the differentiator is rarely another modelling technique. Senior practitioners are paid to translate business problems into data problems, decide what is worth solving and explain the answer to people who may have little technical knowledge.

Lead, principal and head of data science: £100,000+

Prospects puts lead and chief data scientist salaries at £60,000 upwards, with some roles paying £100,000 or more. IT Jobs Watch records a £100,000 UK median for Head of Data Science, although that figure is based on only 11 advertised roles during the period. That makes it useful as an indication rather than a hard benchmark. A small sample can shift significantly when only a handful of vacancies change. Contract data scientists, meanwhile, earn roughly £450 to £500 per day (Prospects).

Data scientist salary in London vs the rest of the UK

London data scientists command a median advertised salary of £80,000, compared with £75,000 nationally (IT Jobs Watch, London). That’s a premium of roughly 7%. The gap is smaller than many people expect, particularly given London’s substantially higher living costs. The more interesting differences appear when you look at specialist skills.

• PyTorch roles pay a median of £96,250 in London compared with £85,000 across the UK (IT Jobs Watch, PyTorch).
• Apache Spark roles pay £95,000 in London against £80,000 nationally (IT Jobs Watch, Apache Spark).

So London doesn’t simply pay more for every data scientist. It contains a greater concentration of specialist roles that already attract higher salaries. For someone deciding where to invest their time, the implication is fairly clear: the skill premium may matter more than the postcode premium.

Data scientist vs data analyst: where the pay steps up

  Before committing to a retraining route, it helps to see the whole ladder. Data analyst salary UK figures sit well below the data scientist median, in London and nationally.

Role	What it does	UK median	London median	Route in
Data analyst	Answers business questions with existing data: reporting, dashboards, insight	£48,500	£57,500	LSE Data Analytics Career Accelerator
Data scientist	Builds models that predict, personalise and automate; owns method and inference	£75,000	£80,000	Cambridge PACE Data Science with Machine Learning & AI Career Accelerator
Head of data science	Owns the function, the roadmap and the team	£100,000 (thin sample: 11 ads)	£100,000	Promotion from senior and lead roles

*Based on a small sample of 11 advertised roles. Source: IT Jobs Watch, Data Scientist, Data Analyst and Head of Data Science, six months to August 2026. The move from analyst to data scientist is worth roughly £26,500 at the median. Importantly, that isn’t simply a seniority premium. It reflects a change in the type of work employers are buying, from analysing existing data to building systems that predict, automate or influence future outcomes. That’s why the skills table below is more useful than the salary table alone. If data analysis is where you want to begin, how to become a data analyst: a beginner’s career roadmap sets out one route.

The skills that raise a data scientist salary in the UK

Employers don’t pay for job titles in isolation. They pay for combinations of skills, and different combinations have different market values. The table below shows median salaries across UK permanent technology roles that mention each skill, alongside the £75,000 data scientist median and the wider £57,500 UK IT median.

Skill premiums in UK advertised permanent roles 

(UK advertised permanent roles, six months to late August 2026)

Skill	UK median	London median	Year on year	Job ads	Share of UK IT vacancies
PyTorch	£85,000	£96,250	+6.25%	237	0.22%
LLM	£85,000	£95,000	−5.56%	1,090	1.00%
Databricks	£85,000	£90,000	+13.33%	1,028	0.94%
MLOps	£83,500	£87,500	−9.73%	386	0.35%
Generative AI	£82,500	£87,500	−5.71%	932	0.86%
Apache Spark	£80,000	£95,000	+11.89%	284	0.26%
Machine learning	£75,000	£90,000	−6.25%	2,405	2.21%
AWS	£72,500	£85,000	No change	7,007	6.41%
Python	£70,000	£85,000	−3.45%	6,971	6.40%
SQL	£57,500	£80,000	No change	6,506	5.97%

Source: IT Jobs Watch skill pages linked above.

Commodity skills vs premium skills

Python and SQL each appear in more than 6,000 UK technology vacancies. Their median salaries sit at or below the data scientist median. That doesn’t make them unimportant. Quite the opposite. They’re foundational. But foundational skills rarely command scarcity premiums. Employers expect data scientists to know Python and SQL in much the same way they expect accountants to understand spreadsheets. They help you qualify for the role, but they don’t necessarily help you command the highest salary. The higher premiums sit in the specialist layer. PyTorch, Databricks, Apache Spark and MLOps all carry higher advertised medians. The difference between SQL and PyTorch is roughly £27,500 at the median, despite both potentially appearing on the same CV.

What’s rising, and what’s normalising?

Not every high-paying skill is becoming more valuable at the same rate. Three of the best-paid skills have seen advertised medians fall over the past year:

1. Machine learning: −6.25%
2. Generative AI: −5.71%
3. LLM: −5.56%

Three others have risen:

1. Databricks: +13.33%
2. Apache Spark: +11.89%
3. PyTorch: +6.25%

Meanwhile, the overall data scientist median is up 7.14%. One possible interpretation is that the market is shifting from familiarity with AI towards the ability to put AI to work. General knowledge of machine learning, generative AI and LLMs has become more common. Skills associated with deploying models, managing data infrastructure and operating at scale remain scarcer. In labour market terms, supply is catching up faster in some skills than others. For someone deciding what to learn next, that suggests putting more weight on deployment, data platforms and applied engineering rather than simply adding another layer of model theory.

The AI premium is real, but uneven

PwC’s 2026 Global AI Jobs Barometer estimates an average wage premium of 62% for workers with AI skills, up from 57% the previous year. It also reports that AI-skilled roles are growing at 9%, compared with a near-flat overall jobs market. At first glance, that may appear to conflict with falling median salaries for LLM and generative AI skills. It doesn’t necessarily. The wage premium for AI capability can widen across the labour market even as the premium for individual AI-related tools compresses within data science. As more people acquire basic familiarity with a technology, employers become less willing to pay simply for knowing it exists. The more durable combination is applied technical capability plus commercial judgement. If you’re more interested in leading AI adoption than building models yourself, the LSE AI Leadership Accelerator is designed for that route.

What UK data scientist job ads actually ask for

Salary data shows what the market pays. Vacancy data shows what employers actually request. The skills table shows what pays across the whole market. Job ads show what hiring managers request. In London data scientist adverts, the most-cited requirements are (IT Jobs Watch, London):

Requirement	Share of London data scientist ads
Machine learning	52.03%
AI	49.59%
Python	41.06%
Analytics	36.18%
Academic degree	19.92%
MLOps	19.51%
Natural language processing	17.07%
CI/CD	14.63%
LLM	14.63%
AWS	14.23%

One figure is particularly striking: an academic degree is explicitly mentioned in fewer than one in five London data scientist adverts. MLOps appears almost as frequently, while CI/CD appears in roughly one in seven. That doesn’t mean qualifications have become irrelevant. It does suggest that employers increasingly value evidence that candidates can build, deploy and maintain systems rather than simply understand the underlying theory.

The highest value skills aren’t all technical

As salaries rise, the differentiator increasingly moves beyond tooling. Four capabilities become particularly important:

1. Problem framing. Translating a business question into a data problem worth solving.
2. Communicating uncertainty. Explaining results clearly without overclaiming or hiding behind caveats.
3. Commercial prioritisation. Knowing which model is worth building in the first place.
4. Governance and ethics literacy. Understanding how to work within increasingly complex AI regulation and organisational controls.

PwC’s research supports the broader trend. Entry-level roles most exposed to AI are seven times more likely to require traditionally senior human skills. Those roles have grown 35% since 2019, while other entry-level roles have fallen 10% (PwC). In other words, the technical floor is rising, but so is the value of judgement. That’s particularly important for career changers, who may already have years of commercial or sector experience to bring into a data role. See why transferable skills are the key to success.

Are data science jobs still in demand in the UK?

Data scientist roles jumped 180 places in the IT Jobs Watch demand rankings year on year, reaching 591 UK permanent vacancies (IT Jobs Watch). The UK has also been estimated to have between 178,000 and 234,000 unfilled data specialist roles (gov.uk), with the wider skills gap estimated to cost the economy around £57 billion a year in lost output (HR Review). But there’s another side to the market. UK data scientist vacancy volumes remain below their 2021 to 2022 peak, and junior positions are highly competitive. The shortage isn’t simply of people who’ve completed a data science course. It’s of people who can take a problem from raw data to a useful business outcome. That difference should shape both how you learn and how you position yourself to employers.

How to move up a data scientist salary band in 12 months

1. Benchmark against the market, not your team. Compare your salary with external percentile data. If you have three years’ experience and earn below the £60,000 25th percentile, that’s useful market evidence, not just a sense that you may be underpaid.
2. Choose one premium skill rather than four. Focus on an area where demand remains strong, such as Databricks, Spark, MLOps or model deployment. Depth tends to create more value than a long list of introductory skills.
3. Build proof while you learn. A deployed model with a measurable business outcome gives an employer more information than a certificate alone. See how project-based learning accelerates your career.
4. Add a credential employers recognise. University-partnered programmes can provide an additional signal, particularly for career changers who don’t yet have a data science job title. See do employers value university certificates?
5. Work on a real business problem. An employer project gives hiring managers something concrete to question, assess and verify. That’s different from completing an exercise where the problem, data and desired outcome have already been defined. Read the head start most career changers don’t know exists.
6. Negotiate against the market rate. Take salary data for your role, skills and location into the conversation. Negotiating solely from your current salary risks allowing yesterday’s pay to anchor tomorrow’s.

Where to build the skills that carry the premium

The skills attracting higher salaries in the market, including applied machine learning, deployment and working with real data at scale, sit at the centre of the Cambridge PACE Data Science with Machine Learning & AI Career Accelerator. Four elements distinguish it from a typical self-paced course:

1. A University of Cambridge PACE certificate. A recognised university credential rather than a platform-issued badge.
2. An Employer Project. A live business challenge designed to produce practical portfolio evidence.
3. One-to-one career coaching. Support throughout the programme rather than a single careers session.
4. Part-time study over seven months. Designed to fit alongside a full-time job, with an expected commitment of 15 to 20 hours per week.

You can read more about how a Career Accelerator works or, if time is the main concern, explore whether you can change careers while working full time.