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Data Science & Machine Learning

Overview

Data scientists build statistical models and machine learning systems to extract predictive or descriptive insight from data. In practice the role blends data engineering, statistics, programming, and domain knowledge. Many data scientists in UK companies spend more time on data cleaning, exploration, and stakeholder communication than on model training. A junior data scientist who can deliver reliable analysis and communicate uncertainty clearly is more valuable than one who can name every model architecture but cannot debug a pandas pipeline.

What does the Data Science & Machine Learning role involve?

Extracting and preparing datasets from databases, APIs, and flat files.
Exploratory data analysis to understand distributions, correlations, and anomalies.
Building, evaluating, and iterating on statistical and machine learning models.
Writing explicit documentation of methodology and assumptions.
Working with engineers to deploy models into production or integrate outputs into reporting.




Presenting findings to technical and non-technical audiences.
Maintaining and monitoring deployed models for drift and degradation.

Skills Required

Python (pandas, NumPy, scikit-learn, matplotlib).
SQL for data extraction and transformation.
Statistics: distributions, hypothesis testing, regression, and confidence intervals.
Machine learning fundamentals: supervised learning (regression, classification), unsupervised learning (clustering), model evaluation metrics.
Data visualisation: matplotlib, seaborn, or Plotly.
Version control with Git.
Understanding of cloud-based data tools (BigQuery, AWS S3/Athena, Azure ML).

UK Salary Range

Entry level (0-2 years): £28,000 to £40,000. Graduate data scientist and junior data analyst with ML focus. Higher end at fintech and tech companies.
Mid-level (2-5 years): £45,000 to £65,000. Independent ownership of ML projects from data to deployment. Expectation of strong Python engineering al

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