Harnham
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London, Greater London
Marketing Data Scientist
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Job Type: Full-time |
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Salary: £35,000 per annum |
Overview
Marketing Data Scientist Central London Up to £45,000 Bonus Benefits A global data science function based in London are seeking a Marketing Data Scientist to join their expanding team. This company work with some of the biggest names globally within retail, electronics and social media. You will be involved in advanced analytics/data science projects to do with their marketing spend and campaigns, including using SQL and R/Python daily. Duties and Responsibilities - Marketing Data Scientist Design, plan and execute the targeting of customers through the company's targeted marketing programme Create and establish advanced customer analytics - e.g. segmentation, cohort analysis and customer retention Using statistical modelling methodologies to solve queries and optimise the marketing mix Manage and conduct regular post campaign analysis, delivering actionable insights and recommendations using tools such as SQL, R and Python. This will include attribution and marketing mix modelling Skills and Qualifications - Marketing Data Scientist University degree in a numerate discipline - ideally mathematics, statistics, engineering etc Experienced user of either SQL, R, Python - specifically modelling Prior experience in development of targeting and customer selections Strong commercial awareness and ability to interact with various internal and external stakeholders Ability to carry out a senior role within the team, as well as working independently to meet tight deadlines Willing to learn new frameworks and languages as needed Salary and Benefits - Marketing Data Scientist Up to £45,000 Bonus Benefits How To Apply - Marketing Data Scientist Please register your interest by submitting your CV via the apply link on this page. Key Words: SQL, R, Python, Data Scientist, Insight Analysis, Customer Insight, Customer Behaviour, Loyalty Analytics, Commercial Insight, Marketing Insight, Marketing Analytics, Segmentation, Clustering, Cluster Analysis, Regression, Propensity, Statistics, Statistical, Statistical Modelling, Modelling, London