Research Data Scientist (Retail)

TL;DR

Research Data Scientist (Retail): Applying machine learning and statistical techniques to solve complex business problems for global retailers with an accent on insight automation, product assortment, and generative AI. Focus on researching and implementing new science-based solutions, performing exploratory data analysis, and validating models to drive measurable value for clients.

Location: London

Company

A global leader in Customer Data Science, partnering with ambitious retailers and brands to put customer insights at the heart of decision-making.

What you will do

• Develop new science-based solutions and modules for application across global client datasets.
• Apply machine learning techniques including regression, clustering, tree-based ensembles, and NLP.
• Perform exploratory data analysis to characterize and visualize complex datasets.
• Write efficient science code using Python and Spark to support product development.
• Identify new opportunities for data science solutions within the retail space.
• Collaborate with senior team members and stakeholders to ensure project success and quality standards.

Requirements

• Master’s degree in Computer Science, AI, Machine Learning, Statistics, Physics, Engineering, or a related field.
• Proven experience with machine learning techniques and their implementation via libraries.
• Strong programming skills in Python and ability to handle large volumes of data using tools like Spark, Hadoop, or SQL.
• Experience with open-source ML packages such as Pandas and scikit-learn.
• Willingness to present technical work to both technical and non-technical audiences.

Nice to have

• PhD in a relevant quantitative field.
• Prior experience in the retail sector.

Culture & Benefits

• Comprehensive rewards package typical of a leading technology company.
• Flexible working hours and personal flexibility.
• Thoughtful perks including your birthday off.
• Access to cutting-edge technology with a nimble, small-business team environment.
• Strong commitment to diversity and inclusion through various employee networks.