Data Scientist, Analytics

Salary: £49,000 - 77,000 per year

• PhD, Masters, or Bachelors degree in Mathematics, Statistics, Computer Science, or a related technical field; or equivalent related professional experience
• 4–6 years of experience in a data science or analytics role (with a relevant degree), or 7+ years of comparable professional experience in a data analytics role
• Demonstrable experience delivering data-driven insights that drove meaningful change or performance improvement across multiple projects using varied analytical techniques
• Advanced proficiency in SQL, Python, or R for data extraction, transformation, and visualisation at scale
• Proficient understanding of statistical concepts including regression, ANOVA, probability, and frequentist vs. Bayesian approaches, with the ability to distinguish statistically significant results from exploratory analysis
• Experience applying a range of modelling techniques (e.g. linear and logistic regression, clustering) and iterating on models to improve accuracy and business relevance
• Proficient communication skills, with demonstrated ability to present clear data stories and insights to audiences of varying technical levels
• Preferred:
  Experience in supply optimisation, pricing, marketplace analytics, or a related domain
• Preferred:
  Familiarity with big data querying tools such as Presto, Hive, Big Query, or Hadoop
• Preferred:
  Exposure to Bayesian methods, causal inference, or multi-armed bandit approaches
• Preferred:
  Experience collaborating with Machine Learning Data Science teams to validate and scale models for business impact
• Preferred:
  Familiarity with inclusive data visualisation design principles, including accessible colour selection and charting best practices

• Apply advanced statistical and machine learning techniques to supply optimisation challenges, delivering data-driven insights and recommendations that create measurable business impact
• Extract, structure, and transform data from multiple sources independently to build datasets suited for modelling and in-depth analysis
• Design and execute measurement frameworks including A/B testing, causal impact analysis, and multivariate methods selecting the appropriate technique based on the business question and clearly communicating trade-offs
• Build, evaluate, and iterate on statistical models (e.g. regression, clustering, classification), correctly interpreting outputs and translating findings into actionable recommendations
• Develop clear, audience-appropriate data visualisations and narratives that communicate insights to both technical and non-technical stakeholders
• Lead small analytical work streams end-to-end, partnering with stakeholders to refine requirements, agree on scope, and evolve the approach based on findings
• Automate repeated measurement and reporting tasks and build scalable dashboards, enabling self-serve analytics for stakeholders across the business
• Produce high-quality project artefacts including technical documentation, presentations, and executive summaries tailored to the appropriate forum and audience
• Collaborate openly with analytics peers, domain experts, and business stakeholders to validate approaches, share knowledge, and socialise findings
• Provide coaching and constructive feedback to junior team members on statistical techniques, visualisation best practices, and data quality standards
• Champion reproducibility by writing shareable, well-documented code and contributing to shared repositories such as Git Hub or Confluence

• Big Data
• Big Query
• Git Hub
• Hadoop
• Hive
• Support
• Machine Learning
• Network
• Python
• SQL
• ARM
• Confluence

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