Senior Data Scientist


London, UK

Oxford, UK


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Machine Learning Research Engineer who will work as part of the Nielsen Connect Data Science ML Research Team.

This is a multidisciplinary team of Nielsen data/domain experts, data scientists and data engineers. It’s mission is to employ the latest advancements in machine learning, deep learning and AI to automate the Nielsen business through rapid agile prototyping and partnerships with the business.

You are an expert in applying machine learning to a wide range of business problems, and have proven experience with researching and deploying machine learning models applied to text and image data to end users. We operate at scale, so you should be able to train models to be able to handle large datasets across multiple countries and languages covering thousands of product attributes.

The role is responsible for the research and development of machine learning algorithms and to deploy prototypes which can be applied to automate and create efficient operational business processes. You are an innovator at heart who is motivated by being set a challenging problem and can work as part of a close-knit team to invent cutting edge solutions which make a real difference to the business. You will not be afraid to learn and apply new techniques to solve a problem. You should have knowledge of and interest in the latest research and advancements in AI, and, crucially, understand how to “take them out of the lab” to move beyond research to practical application.

The successful candidate will be responsible for carrying out R&D and deploying models as scalable prototypes for user evaluation. In cooperation with our Operations and Technology teams, your focus will be to research, develop, evaluate and deploy machine learning algorithms that can automate the generation and transformation of Nielsen’s global sales and product reference data. The team uses agile development principles to incrementally experiment, learn, build and ship prototypes for user evaluation.

This role has a big emphasis on practical application of machine learning over pure research. Therefore the candidate should understand and know how to solve the practical challenges of building machine learning products which deliver a real return on investment and achieve efficiency goals. It means that you should not just be able to write code for small scale experimentation in a Jupyter Notebook, but be able to engineer it into a scalable solution and packaged up as a “data product” delivered to and used by end users.

The ideal candidate will be based in London/Oxford, UK.


  • Work with internal business stakeholders to understand operational processes and requirements and translate into data driven solutions.
  • Research and develop machine learning models and algorithms which can solve defined business challenges to agreed success criteria
  • Apply rapid prototyping agile principles to quickly move models from research to scalable data products to be tested with end users
  • Design experiments to measure the impact on productivity of developed models
  • Build and maintain data pipelines and products using the Python scipy stack, pandas, apache airflow/luigi and parquet.
  • Analyse internal and external data sets using data analysis tools including Python, Jupyter and dash.
  • Develop machine learning models to perform product data attribution from text and image
  • Build scalable training and inference data pipelines which can be deployed as prototype data products for testing with end users
  • Collect and manipulate internal Nielsen and external data from databases, APIs and using web scraping to augment data available for product attribution
  • Document results and present findings to stakeholders across the business


  • You will have at least an MSc in a quantitative subject such as Statistics, Mathematics, Physics, Computer Science, AI or Machine Learning.
  • You will have experience at all stages of data science; problem definition, data acquisition & wrangling, modelling, feature engineering and deployment.
  • Several years experience of developing and deploying machine learning algorithms into production, and expertise in some, if not all, of the following areas: Statistics, Machine Learning, Deep Learning & AI.
  • Solid experience and understanding of supervised and unsupervised learning; machine learning algorithms including neural networks, word embeddings, linear regression, decision trees, random forest, gradient boosted trees, SVM etc; NLP.
  • Proven experience with building deep learning models applied to text and image data. Techniques used should include embeddings; convolutional networks; recurrent networks; sequence data.
  • Python machine learning Libraries including: Scikit-learn, Xgboost, PyTorch/TensorFlow, TensorBoard.
  • Experienced with building data pipelines which use the Python scipy stack including: scipy, numpy, pandas, airflow, luigi, parquet. Proven ability with building training pipelines that can scale to handle large datasets and multiple (100s/1000s) of models is required.
  • Coder with strong experience with building Python solutions using OOP principles. Experience of packages such as Flask, Celery, Airflow, d3.js, dash etc to build APIs and data products. Ability to work within an agile development framework, and some experience of agile methodologies such as Scrum are desirable.
  • Experience of working with relational database platforms including SQL Server, MySQL and SQLLite and good TSQL scripting skills.
  • Ability to extract data from third party APIs and RESTful services. Some prior experience of web scraping would be preferred.
  • Excellent written and verbal communication skills and experience of presenting findings to stakeholders.
  • Track record with working in a business to understand business needs, translate them into specific requirements, and build solutions which meet those requirements.
  • If it sounds interesting to you, do not hesitate to apply as it might be a perfect match for you!


We’re in tune with what the world is watching, buying, and everything in between. If you can think of it, we’re measuring it. We sift through the small stuff and piece together big pictures to provide a comprehensive understanding of what’s happening now and what’s coming next for our clients. Today’s data is tomorrow’s marketplace revelation.

We like to be in the middle of the action. That’s why you can find us at work in over 100 countries. From global industry leaders to small businesses, consumer goods to media companies, we work with them all. We’re bringing in data 24/7 and the possibilities are endless. See what’s next with us at Nielsen:


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