WorldQuant develops and deploys systematic financial strategies across a variety of asset classes and global markets
We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on exploiting market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a sustainable, global investment platform.
Technologists at WorldQuant research, design, code, test and deploy projects while working collaboratively with researchers and portfolio managers. Our environment is relaxed yet intellectually intense. Our teams are lean and agile, which means rapid prototyping of products with immediate user feedback. We seek people who think in code, aspire to solve undiscovered computer science challenges and are motivated by being around like-minded people. In fact, of the 600 employees globally, approximately 500 of them code on a daily basis.
WorldQuant’s success is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Great ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess a mindset of continuous improvement. That’s a key ingredient in remaining a leader in any industry.
Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an exceptional talent. There is no roadmap to future success, so we need people who can help us create it. Our collective intelligence will drive us there.
We are looking for a Python developer with profound experience in Data Science and NLP who will help us to build a custom search engine for our company’s data and perform text-processing tasks to turn unstructured alternative data into enriched investment ready datasets. Task is to develop information retrieval system for performing fast and efficient full-text search in hierarchical set of short and multilingual documents with a lot of "out of vocabulary" words. Documents form tree structures with several documents in one node. The goal is to find the most relevant tree and one of its nodes in response to a possibly noisy query.
What You’ll Bring:
3 года (Обязательно)