Experts who transform raw data into actionable insights. A core role in the data-driven decision-making process of modern organisations.
Data Scientists are the linchpins of data-driven organisations, transforming vast amounts of raw data into meaningful insights that inform strategic decisions. They work across a range of industries, from finance and healthcare to retail and technology, using statistical models, machine learning, and data visualisation to uncover patterns and trends. Whether in a startup or a large corporation, Data Scientists play a crucial role in driving innovation and optimising business performance.
A typical week for a Data Scientist is a mix of data exploration, model building, and stakeholder communication.
Based on advertised midpoints across the 193 UK listings priced in pounds in the last 12 months. Base salary only.
% of 238 listings posted in the last 12 months that mention each skill, extracted from job descriptions.
A typical UK progression for data scientists. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.
Assist in data collection and cleaning, and support more senior team members in model development and analysis.
Own specific projects, develop and validate models, and communicate insights to stakeholders.
Lead complex projects, mentor junior team members, and influence strategic decisions with data-driven insights.
Drive the overall data strategy, lead a team of data scientists, and collaborate with C-level executives.
There's no single route, but most people follow some version of these steps.
Gain a strong understanding of statistics, programming, and data manipulation. Complete relevant courses or a degree in a quantitative field.
Work on personal or open-source projects to build a portfolio that showcases your data science skills and expertise.
Secure internships or entry-level roles to gain hands-on experience in a professional setting. Learn to work with real-world data and business challenges.
Choose a specific industry or domain, such as healthcare or finance, and deepen your knowledge and skills in that area.
Take on leadership roles, manage projects, and mentor junior team members. Develop your communication and management skills.
Collaborate with C-level executives to drive the organisation's data strategy. Influence key business decisions with data-driven insights.
Adecco London £600 – £750 pd
Develops and deploys machine learning models and data pipelines that shape decision-making, customer service and business performance. Works closely with ML and software engineers on integration and coding standards, and engages stakeholders to translate complex business problems into data science solutions. Retail context with scope for personalisation, recommendation and experimentation work.
M4 Partners Stratford-upon-Avon
Hands-on product development role building and deploying machine learning and AI capabilities for an in-house AI platform, covering information extraction, classification, retrieval, summarisation and decision support. Focus on reliable structured JSON outputs, evaluation datasets, testing for accuracy, latency and cost, plus API integration with developers. Fully remote, outside IR35.
Innova Solutions London £50 pa
Applies statistics and time-series analysis to mobile app telemetry, turning raw data into decision-ready insights and dashboards for leadership. Builds multi-page Streamlit dashboards and integrates API/SQL sources including Snowflake and OpenSearch, working end-to-end from data source to analysis to dashboard alongside QE, delivery and product teams.
Michael Page Leeds £40,000 – £48,000 pa
Analyse large datasets to uncover trends and build predictive models and algorithms that support strategic decisions in a financial services setting. Work within a collaborative analytics team, presenting findings to non-technical stakeholders and helping maintain data pipelines and infrastructure. Requires Python, R or SQL plus statistical and machine learning techniques, with GIS tooling also desirable.
ARM Manchester
Day-to-day work centres on analysing and reconciling large finance datasets in SQL, investigating data quality issues across multiple systems, and defining business rules, mappings and acceptance criteria. The role also covers testing, validation and release support, producing analysis for technical and non-technical stakeholders while working with finance, risk, data and technology teams.
NES Group Ltd London
Leads customer analytics across multiple markets, building CLV, RFM segmentation, incrementality measurement and value frameworks that give leadership defensible commercial insight. Partners with Finance to reconcile customer, transaction and margin data, and builds scalable pipelines in Databricks, Spark and Python with strong validation and audit practices.
DCV Technologies Manchester £550 – £600 pd
Contract role developing and evaluating AI/ML solutions, including classical models and LLM-based applications on Google Cloud Platform. Involves prompt engineering, RAG concepts, experiment design, model evaluation and documentation, plus requirements gathering, code review and troubleshooting alongside project managers and developers.
BDO London
Builds and productionises AI/ML models for a regulated financial client, covering data wrangling, feature engineering, model development and validation, and containerised deployment. Works with an existing delivery team on classification, outlier detection, ranking and time-series risk analysis, with documentation and reproducible Git-based workflows. RegTech, FinCrime and graph analytics experience is advantageous.
The locations with the most live listings for this role today.
Essential skills include strong statistical knowledge, programming proficiency (Python, R), data manipulation, and communication skills.
Gain relevant skills through courses or a degree, build a portfolio, and seek internships or entry-level roles to gain practical experience.
The typical progression is from Junior Data Scientist to Data Scientist, Senior Data Scientist, and finally Principal Data Scientist.
Key responsibilities include data analysis, model development, stakeholder communication, and staying updated with the latest research and tools.
Salaries vary based on experience and location. For more detailed salary information, please refer to the salary section on this page.
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