Machine Learning Engineer / Data Scientist

Job description

In a world drowning in unstructured date, we need ML specialists with a strong pragmatic and execution focus to help us build products that generate actionable intelligence for human analysts.  More, we need people with specific expertise in NLP, Graph Theory and Time Series Analysis, or other comparable computational statistics skills.


We have a small but growing team with decades of experience applying ML and AI in the real world. We have academic relationships with many of the UK’s leading ML universities where we aim to apply cutting edge research as soon as it is viable to real world, client-led problems.


Along with evidence of an academic background in Machine Learning or Data Science, we are looking for people with an ability to use theoretical models in the real world on real data. We are engineers and product developers as well as scientists. Our products solve customer problems using entity extraction and disambiguation and link prediction.  We are also increasingly focussing on forecasting events.


We have a bias towards previous software development experience in Python and to a lesser extent Java, R, Matlab and Octave, along with experience in primary open source tools related to the discipline you have experience of (e.g. Pandas for time series, NLTK / spacy / Gensim for NLP). But do not let that put you off if you have expertise in one of the relevant fields list above.  We will work with you to understand what training and support needs you have, and where relevant provide budget to attend conferences. 


You will work with a supportive team committed to helping you do your best work. We hold quarterly hackathons, where we take a break from the sprint rhythm, take some of those blue-sky ideas, and see if can make something happen! 


Adarga is one of the few companies in the UK making Artificial Intelligence work on the ground, today, in real products. If you have a passion for making products that enable humans to analyse vast amounts of unstructured data faster, more accurately and more effectively then why not join us?