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Machine Learning Data Analyst - Funds & Portfolio

Scheme at Bloomberg

Machine Learning Data Analyst - Funds & Portfolio

Scheme at Bloomberg

  • LocationLondon, UK
  • Position levelScheme
  • Job period iconFull-time employment
  • Application deadline iconApply by 27 Dec, 2018
  • Application deadline iconPosted on 28 Oct, 2018
  • Start date iconStart date:
  • Visa iconVisa Sponsor
  • CompetitiveCompetitive

Overview

Bloomberg runs on data, and in the Global Data team we're responsible for acquiring it and providing it to our clients. We collect, analyse, process and publish the data which is the backbone of our iconic Bloomberg Terminal- the facts and figures which ultimately move the financial markets. We apply problem-solving skills to identify innovative workflow efficiencies and we implement technology solutions to enhance our systems, products and processes- and all this while providing platinum customer support to our clients.

Our Funds & Portfolios team is responsible for the full lifecycle of data relating to all types of funds and collective investment schemes across Europe, Middle East, and Africa. We provide the financial community with high quality fund descriptive and pricing data, fund portfolio holdings as well as regulatory information through the Bloomberg terminal and Bloomberg Enterprise Solutions, creating a powerful platform for detailed analysis of funds.

What’s the role?

You're the type of person who loves unstructured data. You have probably run projects 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. 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. You enjoy designing workflows and thinking about the end-to-end implementation of production technologies to improve our processes.

A key aspect of our work as part of Bloomberg's Global Data department is finding, extracting, and interpreting data in fund documents. To aid this process we develop machine learning algorithms to classify documents or extract key pieces of information most relevant for our clients. 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.

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. We expect you to take lead on your work and collaborate with stakeholders to find opportunities for the application of NLP in our product.

We will trust you to:

  • Come up with innovative applications of NLP in our business
  • Work in our big data and ML platform (HDFS/Spark, GPUs)
  • Liaise with internal stakeholders
  • Write production code (predominantly Python)
  • Get hands on with our data and really understand the content we work with

We expect you will have at least 2 of:

  • Academic qualifications in a quantitative subject
  • Demonstrable understanding of machine learning
  • Demonstrable software engineering skills
  • Experience of technical project and stakeholder management

Does this sound like you?

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 access2@bloomberg.net. Alternatively, you can get support from our disability partner EmployAbility, please contact +44 7852 764 684 or info@employ-ability.org.uk.

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Job location

3 Queen Victoria St, London, UK

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