Senior Data Scientist

Senior Data ScientistSan Francisco/AustinResponsibilitiesParticipate and guide all aspects of the project lifecycle from requirements gathering, data acquisition and preparation, hypothesis generation, ideation, coding, testing, and deployment of data science productDevelop state of the art machine learning models. Iterate to push the boundaries on what problems can be solved through data scienceConduct open-ended data exploration to evaluate and improve internal processesCollaborate with business partners to identify opportunities for innovationCollaborate with data engineers to identify data preparation/cleansing/ETL pipelinesCreativity in formulating statistical questions to address business needsDocument projects including business objective, data gathering, and processes, approaches, final algorithm, detailed set of results and metricsWork closely with engineers, product managers and other data scientists to develop algorithms, and initiate features and modeling discussionsMentor team members to help them elevate to the next levelBackground and ExperienceWe welcome applicants from a variety of backgrounds and experiences. Below gives you a sense of how we're thinking about what you'll need to be successful in the role.BS, MS or Ph.D. in Computer Science, Statistics, Engineering, or a related quantitative field4+ years of industry experience in productionizing machine learning models4+ years of experience in writing production codes using programming language (Python), and scripting languages (Unix shell) as well as solid experience with gitMastery of Python, data wrangling libraries (Pandas, Numpy, etc.), and machine learning libraries (Scikit-learn, MLlib, Tensorflow, Keras, etc.)Excellent statistical intuition and knowledge of various approaches such as inference, hypothesis testing, sampling, etcExcellent communication skills, both written and oral; comfortable presenting findings and recommendations to decision-makersExcited about working with large data setsDeep understanding of machine learning models. Demonstrable experience applying these methods to real data problems such as classification, regression, recommendation system, time series analysis, etcAbility to deploy and scale models into a production environment as microservices or on AWSExperience with Jupyter notebook environment.

Experience with text data and NLP is a big plusExperience with SQLBonus PointsExperience with AWS ecosystem and SparkExperience with Flask or similar web-based servicesExperience with Deep LearningExperience with data warehouses, data lakes, and ETL