The demand for Data Scientists (or people in this space in some way) is increasing, with a notable shortage of skilled professionals – particularly within emerging technologies – making experienced and talented Data Scientists highly sought after.

A wide range of industries rely on Machine Learning and Data Science expertise, including:
Financial Services, Healthcare, Government, Marketing & Sales, Transport and more.

Chief-Level Roles:
Chief Analytics Officer | Chief Data and Analytics Officer | Chief Scientist, Global Head of Analytics | Chief Research & Analytics Officer | Chief Credit & Analytics Officer | Chief Technology Officer, Enterprise Information Management & Analytics

Director-Level Roles:
Director of Analytics | Director – Advanced Analytics | Director – Predictive Analytics | Director, Customer Insights & Business Analytics | Director, Digital Analytics | Director of Risk Analytics & Policy | Director, Business Intelligence & Analytics | Director, Web Analytics | Director, Clinical Analytics | Director of Product, Analytics | Director of Marketing Analytics | Director of Data Science & Analytics Practice | Director of Fraud Analytics & R&D | Director of Innovation, Big Data Analytics | Director of Statistical Modelling and Analytics

Manager-Level Roles:
Analytics Manager | Data Science Manager | Business Analytics Manager | Marketing Analytics Manager | Customer Analytics Manager | Risk & Analytics Manager | Predictive Analytics Manager | Strategic Analytics Manager | Web Analytics Manager | Clinical Analytics Manager

Specialist Roles:
Data Scientist | Statistician | Statistical Modeller | Quantitative Analyst | Risk Analyst | Marketing Analyst | Advanced Analytics Manager

• Junior Data Scientist
• Data Scientist
• Chief/Lead Data Scientist
• Perhaps – Data Science Manager
  Head of/Director of Data Science

We’ve outlined the latest salary benchmarks for Data Science roles in the guides below:

Data, Insight and Analytics Salary Guide

Data, Insight and Analytics Day Rate Guide

Data, Insight and Analytics Within Data Engineering Development Salary Guide

Data, Insight and Analytics Within Data Science Salary Guide

The skills required for a successful career in data science typically fall into three categories: programming skills, statistical/analytical skills, and domain expertise.

Programming Skills: Python (including libraries such as Pandas, NumPy, Scikit-learn) | R | SQL and NoSQL Databases | Big Data Technologies (Hadoop, Spark) | Data Wrangling and Cleaning | Cloud Platforms (AWS, Azure, Google Cloud) | Version Control (Git) | Automation and Scripting | Machine Learning Model Development and Deployment

Statistical and Analytical Skills: Descriptive and Inferential Statistics | Hypothesis Testing | Probability Theory | Experimental Design | Predictive Modelling | Time Series Analysis | Data Mining Techniques | Data Exploration and Feature Engineering | Model Evaluation and Validation

Domain Expertise: Understanding Business Context | Translating Data into Insights | Communicating Results Effectively | Collaborating with Cross-functional Teams | Ethical Use of Data | Data Governance and Privacy | Problem-Solving Mindset | Storytelling with Data

Most Data Scientists hold a Master’s (MSc) or Doctorate (PhD) in subjects such as Mathematics, Physics, Statistics, AI, Software Engineering, or Computer Science. Strong technical programming skills are essential, along with a solid foundation in statistics and probability.

For the most part, you’ll also need demonstrable experience working with large datasets and practical use of machine learning techniques and statistical methods

Salary: £28k – £38k
What They Do:
Collects, cleans, and analyses data to identify trends and patterns. Supports senior data scientists with model development and business insight generation.

Skills Needed:

• Strong statistical knowledge
• Programming skills in Python or R
• Data wrangling and visualisation (Pandas, Matplotlib, Tableau, Power BI)
• SQL and basic database knowledge
• Understanding of machine learning fundamentals

Salary: £38k – £55k
What They Do:
Builds predictive models and machine learning algorithms, works with business teams to develop data-driven solutions, and communicates insights clearly.

Skills Needed:

• Advanced Python/R programming
• Machine learning frameworks (Scikit-learn, TensorFlow, PyTorch)
• Strong data visualisation and storytelling skills
• Statistical modelling and A/B testing
• Cloud platforms for model deployment (AWS, Azure, GCP)

Salary: £55k – £75k
What They Do:
Leads projects involving complex data science solutions, optimises models for production, and mentors junior data scientists.

Skills Needed:

• Deep learning and advanced machine learning methods
• Data engineering collaboration for scalable solutions
• Strong software engineering skills (version control, testing)
• Business acumen and translating models into ROI
• Leadership and mentoring abilities

Salary: £75k – £95k
What They Do:
Oversees data science projects and teams, sets strategy for advanced analytics, and ensures data science delivers measurable business impact.

Skills Needed:

• Team leadership and project management
• Model deployment at scale
• AI/ML strategy and roadmap creation
• Stakeholder engagement and executive reporting
• Building data science capability and best practices

Salary: £95k – £120k
What They Do:
Defines the data science vision for the organisation, builds high-performing teams, and drives the adoption of AI and ML solutions across business units.

Skills Needed:

• Department-level strategy and leadership
• Building and scaling AI/ML capabilities
• Cross-functional collaboration and enterprise alignment
• Budget and resource planning
• C-suite stakeholder management

Salary: £120k – £150k+
What They Do:
Owns the company’s data science and AI strategy, ensuring enterprise-wide transformation through data-driven decision-making and innovation.

Skills Needed:

• Executive-level leadership and strategic vision
• Expertise in AI/ML governance and ethics
• Building global data science organisations
• C-suite influence and board-level reporting
• Driving innovation through advanced analytics