Posted Nov 20, 2017 - Requisition No. 63441
You're the type of person who loves unstructured data. You have probably run projects at university or for yourself with data scraped from the web or a social media platform. You are passionate about natural language processing and have practiced applying some of the main NLP methodologies for yourself. You are a fast learner and are excited by the challenge of working in a stripped down Linux environment with access to state-of-the-art computing resources.
A key aspect of our work as part of Bloomberg's Global Data department is finding, extracting, and understanding the data in financial documents. To aid this process we develop machine learning algorithms to classify financial documents or extract key pieces of information our clients are interested in. We use state of the art big data and machine learning technologies such as Spark, GPU clusters and machine learning libraries like scikit-learn and tensorflow in our work.
You will develop algorithms in this technology stack that add meaningful value for our clients. Example projects could include the full range of NLP problems from classification, to named entity extraction, to sentiment analysis. We will expect you to take the lead on your work and collaborate with stakeholders to find opportunities for the application of NLP in our business.
We will trust you to:
Apply if you think we're a good match. We'll get in touch to let you know what the next steps are!
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Bloomberg is a disability inclusive employer. Please let us know if you require any reasonable adjustments to be made for the recruitment process. If you would prefer to discuss this confidentially, please email firstname.lastname@example.org. Alternatively, you can get support from our disability partner EmployAbility, please contact +44 7852 764 684 or email@example.com