Senior Data Scientist

Senior Data Scientist San Francisco/Austin Responsibilities Participate 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 product. Develop state of the art machine learning models. Iterate to push the boundaries on what problems can be solved through data science. Conduct open-ended data exploration to evaluate and improve internal processes. Collaborate with business partners to identify opportunities for innovation. Collaborate with data engineers to identify data preparation/cleansing/ETL pipelines. Creativity in formulating statistical questions to address business needs. Document projects including business objective, data gathering, and processes, approaches, final algorithm, detailed set of results and metrics. Work closely with engineers, product managers and other data scientists to develop algorithms, and initiate features and modeling discussions. Mentor team members to help them elevate to the next level. Background and Experience We 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 field. 4+ years of industry experience in productionizing machine learning models. 4+ years of experience in writing production codes using programming language (Python), and scripting languages (Unix shell) as well as solid experience with git. Mastery 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, etc. Excellent communication skills, both written and oral; comfortable presenting findings and recommendations to decision-makers. Excited about working with large data sets. Deep understanding of machine learning models. Demonstrable experience applying these methods to real data problems such as classification, regression, recommendation system, time series analysis, etc. Ability to deploy and scale models into a production environment as microservices or on AWS. Experience with Jupyter notebook environment. Experience with text data and NLP is a big plus. Experience with SQL. Bonus Points Experience with AWS ecosystem and Spark. Experience with Flask or similar web-based services. Experience with Deep Learning. Experience with data warehouses, data lakes, and ETL. 1872 Consulting