We are looking for a Data Scientist with a strong background in Natural Language Processing and Unstructured Data Mining. Lack of Financial Markets knowledge is not an issue.
- Advanced degree from an accredited college/university in Computer Science, Computational Linguistics, Applied Math or Statistics, Engineering, Bioinformatics, Physics, O.R., or related (strong math/stats background with an ability to understand algorithms and methods from both mathematical and intuitive viewpoints)
- In-depth knowledge of various NLP domains such as entity extraction, speech recognition, topic modeling, machine translation, natural language understanding, parsing, question answering, etc
- Expertise in text mining (probabilistic topic model, word association mining, ontology learning, opinion mining and sentiment analysis, semantic similarity, etc.)
- Expertise in natural language processing/understanding (word representation, sentiment analysis, relation extraction, natural language inference, semantic parsing, etc.)
- Excellent background in machine learning (generative model, discriminative model, neural network, regression, classification, clustering, etc.)
- Experience in deep learning on NLP/NLU is a big plus
- Extensive experiences in using NLP related techniques/algorithms such as HMM, CRF, deep learning & recurrent ANN, word2vec/doc2vec, Bayesian modeling, etc
- Success in building strong ontology / taxonomies
- Strong data extraction and processing skills and experience
- Experience in applied statistics including sampling approaches, experiments, modeling, and data mining techniques
- Experience building analytical models and working with structured and unstructured data sets
- Deep expertise in implementing algorithms in python.
- Experience with data structures and algorithms and ability to work in a Unix environment, processing large amounts of data in a big data environment
- Significant experience building robust data processing and analytics pipelines
- Experience with one or more modern Big Data technology stacks
- Contributions to research communities, e.g. ACL, NIPS, ICML, CVPR, etc. is a Plus
- To develop and apply bleeding edge machine learning algorithms and statistical pattern recognition on extremely large text corpora in the capital markets domain.
- Utilize statistical natural language processing to mine unstructured data, and create insights; analyze and model structured data using advanced statistical methods and implement algorithms and software needed to perform analyses
- Build document clustering, topic analysis, text classification, named entity recognition, sentiment analysis, and part-of-speech tagging methods for unstructured and semi-structured data
- Cluster and analyze large amounts of user generated content and process data in large-scale environments using Amazon EC2, Storm, Hadoop and Spark
- Develop and perform text classification using methods such as logistic regression, decision trees, support vector machines and maximum entropy classifiers
- Perform text mining, generate and test working hypotheses, prepare and analyze historical data and identify patterns
- Generate creative solutions (patents) and publish research results in top conferences (papers)
- Technology Stack for the Resultant Application
- Data Storage + Analytics: AWS/ Cloudera (on-Premise) Hadoop Ecosystem with MongoDB & Elastic Search on S3 or on Premise
- Queueing System: RabbitMQ
- Programming: Python
- Front-End: HTML5 for WebApp and Objective C for ios. Potentially hybrid framework for iOS and Android
- CDN: AWS or On Premise Routing
- Innate curiosity to find commercial insights from large sets of noisy unstructured data.
- Ability to work independently under tight deadlines with accountability.
- Strong results driven personality with a high level of enthusiasm, energy and confidence.
- Strong problem-solving skills.
What We Offer
- Above market compensation.
- A chance to disrupt existing industry paradigms.
- An opportunity to learn from thought leaders.
- Access to an elite global network with strong links to Startup Hubs (Silicon Valley, London, and Canada) and Financial Centres (New York, Singapore & Hong Kong).
- International Travel.