- 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
- 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
- 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.
- 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