Key Responsibilities
Work independently and collaborate with others on the Data Science team to solve a wide variety of challenging business and product needs with innovative data science techniques
Analyze and gain an understanding of the available data sources and their potential to address needs
Collaborate with Product Management, Engineering and DevOps teams to build and deliver high quality, oftentimes deployed solutions
Collaborate with data collection teams to specify criteria for the creation of training and testing datasets
Basic Qualifications
Master’s or Ph.D. degree in Math, Statistics, Computer Science, Machine Learning, Computational Linguistics, Engineering, or a related field
5+ years’ experience developing supervised and unsupervised statistical/machine learning models utilizing both regression and classification techniques such as GLM, CART, Random Forest, GBM, XGBoost, Naive Bayes, SVMs, ANN, CNN, etc.
Fluent in Python (most preferred), R or similar scripting language
Experience building models deployed in a production system
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Preferred Qualifications
Experience with text data and associated best practices for feature engineering/embedding as well as common NLP resources such as: ELMo, GloVe, spaCy, Apache OpenNLP, Stanford CoreNLP, NLTK, WordNet, etc.
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Fluency in Java or other object-oriented programming languages
Technical fluency; comfort understanding and discussing algorithms and their tradeoffs to both experts and non-experts
Excellent critical thinking skills, combined with the ability to present your beliefs clearly and compellingly in both verbal and written form
Ability to adapt in a fast-paced, dynamic environment