Senior Data Scientist for Data-Driven Innovation

1 month ago

London, Greater London, United Kingdom ENGINEERINGUK Full time
About the Role

dunnhumby is a global leader in Customer Data Science, empowering businesses to compete and thrive in the modern data-driven economy.

We're looking for a talented Research Data Scientist who expects more from their career. This is an opportunity to work with a market-leading business to explore new opportunities for us and influence global retailers.

As a Research Data Scientist, you'll work with world-class and passionate people to apply machine learning and statistical techniques to business problems. You'll contribute to the research and implementation of new approaches to address complex problems and perform data analysis and model validation.

  1. Pick up new machine learning approaches, such as regularised regression, clustering or tree-based ensembles, graph-based approaches, natural language processing and neural network techniques, and apply them on client data.
  2. Perform exploratory data analysis to characterise and visualise datasets.
  3. Extend and develop programming skills, in languages such as Python and Spark, to develop efficient science code for science modules.
  4. Help identify new opportunities within the Data Science space for future dunnhumby solutions.
  5. Follow Quality Assurance processes, ways of working and meet coding standards.
  6. Ensure smooth running of your projects, working with senior team members for direction.
Who You'll Get to Work With

Within dunnhumby, you'll primarily work with:

  • Applied and Research Data Scientist teams
  • Data Science Engineering teams
  • Product and Client teams where required
What You'll Need
  1. Master's degree or equivalent in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Statistics, Physics, Engineering, Biology or related field.
  2. Experience with machine learning techniques such as regularised regression, clustering or tree-based ensembles, and the ability to implement them through libraries.
  3. Experience with programming, ideally Python, and the ability to quickly pick up handling large data volumes with modern data processing tools, e.g. by using Hadoop / Spark / SQL.
  4. Experience with or ability to quickly learn open-source software including machine learning packages, such as Pandas and scikit-learn, along with data visualisation technologies.
  5. A willingness to present your work to both technical and non-technical audience and to contribute to the wider data science community.

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