Bloomberg’s Artificial Intelligence (AI) group is a team of engineers and researchers who have a passion for solving complex problems. Their charter: to extract and identify relevant, meaningful, tradeable, and actionable information (such as pricing, earnings, recommendations, and major events) from data (including news, web, social media, and structured data) in real-time, as well as providing advanced ways of accessing this data (such as search, summarization, recommendations, and natural language question-answering). Since their customers rely on this information to make swift financial decisions, they guarantee precision, accuracy, and latency numbers beyond most academic and industry standards.
They aren’t just building customer-facing products, as the infrastructure and algorithms they develop are themselves used across the company. They also publish papers, attend conferences, organize workshops, and contribute back to the larger data science community whenever they can (see https://www.techatbloomberg.com/ai/ and https://bloomberg.com/company/d4gx/).
Who are you?
A research scientist and engineer who wants to work in the areas of machine learning, natural language processing, information extraction, reinforcement learning, graphical models, summarization, information retrieval, question answering, recommender systems, and/or knowledge graphs. You want to join a close-knit group and make a big impact.
They’ll trust you to:
– work with others in the AI group and the company on production systems and applications
– publish research findings in leading academic venues and represent Bloomberg at industry conferences
– write, test and maintain production-quality code, and
– design, experiment, and evaluate algorithms and models
You’ll need to have:
– experience in AI, NLP, ML, Optimization, or related fields
– experience programming in C++, Python or Java, and
– a master’s degree (Ph.D. preferred) with industrial experience
They’d love to see:
– a quantitative background (Probability, Statistics, Linear Algebra, etc.)
– experience with distributed computational frameworks (YARN, Spark, Hadoop, Kubernetes, Docker)
– publications in top-tier conferences or journals (such as ACL, AAAI, SIGIR, KDD, EMNLP, ICML, NIPS or equivalent)
