Do you want to help build an internal data science product that will be used globally across the Wunderman agency? We are looking for a forward thinking data scientist with strong coding skills and experience within NLP and computer vision. You will be seen as the expert in one of these areas and will have the freedom to lead the direction in terms of research and development.

You’ll work alongside the existing data science team, a dedicated engineer and a front end dev team to produce this new product. The candidate will have the freedom to design and build solutions from scratch that will feed directly into a web based front end that we will build in parallel.

Role and responsibilities

Contribute to development of an in house data science product with a focus on methodology ownership
Design and productionise NLP and ComputerVision solutions in a distributed infrastructure
Work hand in hand with engineer to produce microservices/API’s


Wunderman UK


Requirements – Technical

Degree or PhD in Machine Learning, Mathematics, Statistics or Computer Science
Strong Applied Machine Learning Experience productionising algorithms from conception to delivery.
Particular experience required In NLP & Computer Vision
Deep learning (CNNs, LSTMS, Autoencoders)
Object recognition, identification, detection, segmentation (Meanshift, sampling, filtering, global optimisation, edge detection)
Language models (Probabilistic/generative), NER, POS, lexical semantics, topic extraction, Text-to-speech, emotion extraction)
Production level Python
Spark, Hadoop, Apache, Pig, Mongo, Cassandra desirable
Using cloud services to scale and productionise solutions (AWS/Azure/GoogleCloud)
Version Control – Git
Webscraping experience desirable (Scrapy)
Visualisation experience desirable (D3)
Requirements – Non Technical

Strong collaboration and communication skills
An eagerness to develop exciting data science solutions that will have a tangible impact on the business

Educational level:

Ph. D.

How to apply:

Please mention NLP People as a source when applying


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