Data scientist job description.
Looking to hire a data scientist or transition into this high-demand field? This data scientist job description explains core duties like building models, analysing complex datasets, and working with machine learning tools. It also details key skills, growth routes, and salary expectations across experience levels.
What does a data scientist do?
A data scientist uses statistical modelling, machine learning, and advanced analytics to solve complex business problems. They go beyond reporting by building predictive models, discovering patterns, and optimising decisions using large datasets.
Responsibilities include cleaning and preparing data, developing algorithms, running experiments, and visualising findings. Tools typically include Python, R, SQL, Jupyter, and machine learning libraries like Scikit-learn or TensorFlow.
In scale-ups, data scientists often build foundational data models and experimentation frameworks. In enterprise environments, they may focus on specific verticals like pricing, fraud, customer retention, or recommendation systems, collaborating with engineering and product teams.
Key responsibilities of a data scientist.
Data scientists develop models and extract insights to solve complex problems. Common responsibilities include:
-
Analysing structured and unstructured data for trends and patterns
-
Building predictive models and machine learning algorithms
-
Cleaning and transforming large datasets for analysis
-
Collaborating with engineers and product managers to embed models into systems
-
Performing A/B testing, statistical modelling, and hypothesis testing
-
Communicating insights to business teams and leadership
-
Using Python, R, SQL, and data visualisation tools for analysis
-
Creating reproducible experiments and maintaining model accuracy
-
Documenting workflows, assumptions, and findings
-
Supporting data-driven decision-making across departments
This role blends statistical modelling, coding, and cross-functional problem solving.
Skills and requirements for a data scientist.
Data scientists use advanced analytics to uncover business insights. Employers typically look for:
-
3–6 years of experience in data science or quantitative analysis
-
Proficiency in Python or R and statistical libraries
-
Strong background in machine learning and predictive modelling
-
Experience with data wrangling, feature engineering, and testing
-
Familiarity with SQL, cloud platforms, and large datasets
-
Ability to explain models to non-technical teams
-
Understanding of experimentation, A/B testing, and model validation
-
Skilled in communicating uncertainty and assumptions clearly
-
Portfolio of real-world analytical projects or models
Most data scientists come from statistics or engineering backgrounds.
Average salary for a data scientist.
In the UK, the average salary for a data scientist typically ranges from £45,000 to £65,000, depending on statistical modelling, machine learning, and Python/R fluency.
-
Mid-level data scientists earn between £45,000 and £55,000
-
Senior professionals working on predictive analytics or product data may earn between £56,000 and £65,000
-
Bonus potential exists for delivering AI-driven growth or automation projects
Salaries are highest in tech-first organisations, particularly in fintech, retail, and AI scale-ups.
Career progression for a data scientist.
A data scientist uses statistics, machine learning, and programming to model business problems and forecast outcomes. It’s a technical role with high potential for specialisation or leadership. A typical progression includes:
Data analyst / Junior data scientist
Builds exploratory reports and supports modelling under guidance.
Data scientist
Develops models, automates processes, and works with large datasets to uncover opportunities.
Senior data scientist
Leads experimentation, designs algorithms, and collaborates with product and engineering teams.
Principal data scientist / ML lead
Owns project delivery, mentors teams, and aligns models with business performance metrics.
Head of data science
Leads cross-functional data strategy, architecture, and growth-driving data initiatives.
salary guide
Our UK data salary guide.
Data scientists develop models, uncover trends, and solve complex problems with data. Compensation should reflect experience with statistics, tooling, and machine learning.
Our UK data salary guide includes salary benchmarks for data science roles, 2024 comparisons, hiring insight, and salary projections to 2026.
FAQS
Data scientist FAQs.
There’s a shift. Many roles are now closer to decision science — focusing on testing, forecasting, and uplift modelling. Deep learning and AI R&D roles still exist but are concentrated in specific industries like fintech, healthcare, and research-heavy SaaS.
Ask about business impact — not just notebooks. Strong candidates can explain trade-offs, describe model monitoring post-deployment, and reflect on when not to use ML. Projects that shipped, not just Kaggle wins, are the best indicators.
Lack of deployment or product integration. Many report building models that never get used, either due to unclear requirements, lack of stakeholder buy-in, or engineering constraints.
Not entirely. While MLOps is valuable, many teams now split roles — keeping data scientists focused on problem framing, experimentation, and stakeholder alignment, while engineers own deployment and scale.
Some shift into ML engineering, but many take on cross-functional roles like data science lead, product analytics manager, or technical strategy analyst — especially in scaleups or data-first orgs.
