Job Description

Locations

• UK - London

Skills Required

• data scienceintermediate
• data analytics roleintermediate
• SQLintermediate
• supply optimisationintermediate
• big data querying tools such as Prestointermediate
• inclusive data visualisation design principlesintermediate

Required Qualifications

• PhD, Master's, or Bachelor's degree in Mathematics, Statistics, Computer Science, or a related technical field; or equivalent related professional experience (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 (experience, 6 years)
• Demonstrable experience delivering data-driven insights that drove meaningful change or performance improvement across multiple projects using varied analytical techniques (experience)
• Advanced proficiency in SQL, Python, or R for data extraction, transformation, and visualisation at scale (experience)
• 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)
• Experience applying a range of modelling techniques (e.g. linear and logistic regression, clustering) and iterating on models to improve accuracy and business relevance (experience)
• Proficient communication skills, with demonstrated ability to present clear data stories and insights to audiences of varying technical levels (experience)
• PhD, Master's, or Bachelor's degree in Mathematics, Statistics, Computer Science, or a related technical field; or equivalent related professional experience (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 (experience, 6 years)
• Demonstrable experience delivering data-driven insights that drove meaningful change or performance improvement across multiple projects using varied analytical techniques (experience)
• Advanced proficiency in SQL, Python, or R for data extraction, transformation, and visualisation at scale (experience)
• 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)
• Experience applying a range of modelling techniques (e.g. linear and logistic regression, clustering) and iterating on models to improve accuracy and business relevance (experience)
• Proficient communication skills, with demonstrated ability to present clear data stories and insights to audiences of varying technical levels (experience)

Preferred Qualifications

• Experience in supply optimisation, pricing, marketplace analytics, or a related domain (experience)
• Familiarity with big data querying tools such as Presto, Hive, BigQuery, or Hadoop (experience)
• Exposure to Bayesian methods, causal inference, or multi-armed bandit approaches (experience)
• Experience collaborating with Machine Learning Data Science teams to validate and scale models for business impact (experience)
• Familiarity with inclusive data visualisation design principles, including accessible colour selection and charting best practices (experience)
• Experience in supply optimisation, pricing, marketplace analytics, or a related domain (experience)
• Familiarity with big data querying tools such as Presto, Hive, BigQuery, or Hadoop (experience)
• Exposure to Bayesian methods, causal inference, or multi-armed bandit approaches (experience)
• Experience collaborating with Machine Learning Data Science teams to validate and scale models for business impact (experience)
• Familiarity with inclusive data visualisation design principles, including accessible colour selection and charting best practices (experience)

Responsibilities

• 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 workstreams 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 GitHub or Confluence.
• 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 workstreams 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 GitHub or Confluence.

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