Job Overview
The data scientist is the expert who transforms raw data into strategic insights for the business. Positioned between statistician, computer scientist and business analyst, he designs predictive models and machine learning algorithms to optimise performance and inform decisions.
In a startup or scale-up, the data scientist is often versatile, intervening across the entire data value chain. In large companies, he may specialise in a particular domain (NLP, computer vision, recommendation systems, dynamic pricing).
The role evolves rapidly with deep learning, generative AI and AutoML. The modern data scientist must master recent frameworks (PyTorch, TensorFlow, Transformers) whilst maintaining solid statistical foundations.
Key Responsibilities
Data research and collection
Identify relevant data sources (internal databases, APIs, web scraping). Automate collection processes. Evaluate data quality and reliability.
Preprocessing and feature engineering
Clean data (missing values, outliers, duplicates). Transform and normalise variables. Create new relevant features for modelling.
Exploration and analysis
Conduct exploratory data analysis (EDA) to understand distributions and correlations. Detect patterns, trends and anomalies. Produce clear impactful visualisations.
Modelling and machine learning
Build predictive models (regression, classification, clustering). Develop machine learning algorithms suited to the problem. Optimise hyperparameters and combine models (ensemble learning).
Deployment and monitoring
Industrialise models in production (REST API, batch processing). Monitor performance and detect data drift. Continuously maintain and improve models.
Communication and business
Translate technical results into business recommendations. Present insights to business teams and leadership. Collaborate with product managers and engineering teams.
Technical skills vs soft skills
- Python mastery (pandas, scikit-learn, TensorFlow/PyTorch)
- SQL expertise and database manipulation
- Strong statistics and probability knowledge
- Supervised and unsupervised machine learning experience
- Data visualisation tools mastery (Matplotlib, Seaborn, Plotly, Tableau)
- Spark and Hadoop familiarity for big data
- MLOps and model deployment knowledge
- Analytical mindset and scientific rigour
- Curiosity and continuous technological watch
- Hypothesis formulation and experimentation capability
- Excellent communication and storytelling skills
- Business sense and stakeholder understanding
- Autonomy and complex project management
- Critical thinking and bias awareness
Salary Grid 2026
Data scientist salaries by experience (annual gross)
| Experience | SME/Startup | Large Company | London Area |
|---|---|---|---|
| Junior (0-2 yrs) | 40-50K EUR | 45-55K EUR | +15-20% |
| Established (3-5 yrs) | 50-65K EUR | 55-70K EUR | +15-20% |
| Senior (5-8 yrs) | 65-80K EUR | 70-90K EUR | +15-20% |
| Lead/Expert (8+ yrs) | 80-110K EUR | 90-130K EUR | +20-25% |
Salary variation factors
Frequently asked questions about data scientist role
What is the difference between data scientist and data analyst?
Must you have a PhD to be a data scientist?
What are the career prospects for a data scientist?
Is the data scientist role declining with AutoML?
Can someone transition to data scientist from another field?
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