Choose from a wide range of CV templates and customize the design with a single click.
Use ATS-optimised CV and resume templates that pass applicant tracking systems. Our CV builder helps recruiters read, scan, and shortlist your CV faster.
Use professional field-tested resume templates that follow the exact CV rules employers look for.
Create CV
Use professional field-tested resume templates that follow the exact CV rules employers look for.
Create CVThe question “data scientist salary” is rarely just about numbers. It’s about positioning, leverage, market dynamics, and how candidates actually get paid more in real hiring environments.
This guide goes far beyond averages. You’ll understand:
What data scientists actually earn in 2026
Why two candidates with the same title can have a £40K+ difference
How recruiters benchmark your salary within seconds
What hiring managers actually pay for
How to increase your salary strategically (not just by switching jobs)
This is how compensation really works in the modern data science market.
The UK market has widened significantly due to remote hiring, AI demand, and talent scarcity.
Entry-Level (0–2 years):
£35,000 – £50,000
Top-tier candidates (STEM degrees, internships, strong GitHub): up to £60,000
Mid-Level (2–5 years):
£55,000 – £85,000
High-impact contributors (production ML, business impact): £90,000+
Senior Data Scientist (5–10 years):
£80,000 – £120,000
Recruiters do NOT pay based on title. They pay based on signal strength.
Hiring managers ask:
“Can this person make us money, save money, or reduce risk?”
Candidates who show:
Revenue impact
Cost reduction
Product improvement
earn significantly more.
Weak Example:
“Built machine learning models”
Good Example:
“Developed churn prediction model reducing customer attrition by 18%, saving £2.3M annually”
Why this matters:
Salary follows measurable impact, not technical complexity.
There is a massive salary gap between:
Here’s the real internal process:
They look for:
Company names
Tools (Python, SQL, TensorFlow, etc.)
Impact metrics
Career progression
If signals are weak → low salary bracket assigned instantly.
Recruiters compare you to:
Similar candidates recently placed
Internal salary bands
Lead-level / IC excellence: £130,000+
Principal / Staff Data Scientist:
£110,000 – £160,000+
FAANG-equivalent or high-growth startups: £180,000+ (incl. equity)
London premium still exists but is shrinking due to remote work:
London: +10% to +25%
Remote roles (global companies): often outpay London
Regional hubs (Manchester, Bristol, Cambridge): increasingly competitive
Understanding global benchmarks is crucial because UK salaries are now influenced by international hiring.
USA:
Mid-level: $120K – $180K
Senior: $160K – $250K+
Netherlands / Germany:
Remote US companies hiring in UK:
“Jupyter Notebook Data Scientists”
“Production ML Engineers / Applied Scientists”
Production experience adds £20K–£50K premium
Signals include:
Model deployment
MLOps pipelines
Real-time systems
Monitoring & retraining
Generalists earn less than specialists.
High-paying niches:
AI / LLM / Generative AI
FinTech / Quant modelling
HealthTech / Bioinformatics
Recommendation systems
Different companies pay differently for the same role:
Big Tech → highest salaries
Scale-ups → high base + equity
Banks → stable, slightly lower ceiling
SMEs → lowest pay, broader roles
Two identical candidates can earn very different salaries based on how they position themselves.
Recruiters assess in seconds:
Seniority signals
Confidence in impact
Clarity of achievements
Market awareness
Competing applicants
Hiring managers ask:
“Are we overpaying or underpaying relative to risk?”
High-confidence candidates:
Strong portfolio
Clear impact
Good communication
→ get higher offers
If your CV reads like:
“Worked on…”
“Responsible for…”
You will be underpaid.
Listing:
Python
Pandas
Scikit-learn
does NOT increase salary.
Impact does.
If you don’t show:
Leadership
Decision-making
End-to-end responsibility
You get mid-level salaries forever.
Strong theory but no business application = lower offers.
High earners:
Start with business problem
Quantify outcomes
Align with company KPIs
If you want a £20K+ jump:
Deploy models
Learn cloud (AWS, GCP)
Understand pipelines
Not all companies are equal.
High-paying targets:
AI-first startups
Tech companies
US-based remote employers
Top candidates:
Interview at multiple companies
Create competing offers
Negotiate from strength
Recruiters respond to signals, not titles.
You need:
Strategic thinking
Ownership
Leadership examples
Data Analyst: £30K – £55K
Data Scientist: £50K – £120K+
Key difference:
Why:
Closer to production systems
Engineering-heavy
AI Engineers (LLMs, GenAI):
Currently the highest-paying segment.
Candidate: Daniel Carter
Role: Senior Data Scientist
Location: London, UK
PROFESSIONAL SUMMARY
Senior Data Scientist with 7+ years of experience delivering production-grade machine learning solutions across FinTech and eCommerce. Proven track record of driving £10M+ business impact through predictive modelling, recommendation systems, and real-time analytics.
CORE SKILLS
Machine Learning
Python (Pandas, Scikit-learn, TensorFlow)
SQL & Data Warehousing
AWS (SageMaker, Lambda, S3)
MLOps & Model Deployment
A/B Testing & Experimentation
PROFESSIONAL EXPERIENCE
Senior Data Scientist – FinTech Scale-up (London)
2019 – Present
Led development of fraud detection system reducing losses by 27% (£4.1M annually)
Deployed real-time ML pipeline using AWS SageMaker, reducing model latency by 40%
Built customer segmentation model increasing conversion rates by 22%
Mentored 4 junior data scientists and led cross-functional ML initiatives
Data Scientist – eCommerce Company
2016 – 2019
Built recommendation engine increasing average order value by 18%
Designed churn prediction model improving retention by 15%
Automated reporting pipelines reducing manual workload by 60%
EDUCATION
MSc Data Science – University of Manchester
KEY PROJECTS
Real-time recommendation system (millions of users)
Fraud detection using gradient boosting & anomaly detection
Quantified impact everywhere
Shows ownership and leadership
Demonstrates production experience
Aligns with business outcomes
They ask:
“What is the impact expectation for this role?”
Top candidates speak in:
Revenue
Cost savings
Growth metrics
They position themselves as:
Strategic hires
Not just technical contributors
Short answer: Yes, but unevenly.
AI / LLMs
Real-time ML systems
Applied AI in business
Pure analysis roles
Non-production ML
Generic skillsets
Salary is driven by impact, not tools
Production experience is a major multiplier
Positioning determines compensation
The market rewards specialists and business thinkers
Upload CV
Import from Linkedin
