Work with the Data team and cross-functional stakeholders (Product, Customer Success, Engineering, Sales, Marketing and Finance) to use data and turn Databricks business and operations data into insights for product design, strategies for customer acquisition/retention, and optimizations to engineering system efficiency/stability/performance; Manage own project end-to-end from requirements gathering, data exploration to presenting insights to stakeholders and/or deployment an algorithm in a production environment. Detailed job duties include: Produce modularized code for data engineering and operationalized the pipelines with tools like Airflow; Design metrics and visualized data with dashboarding in Databricks; Apply advanced statistical/causal inference methods to measure the impact of feature launches on revenue and product performance; Implement machine learning/deep learning algorithms for use cases such as customer profiling, churn predictions, and runtime estimations. $130,000 - $180,000 per year. Minimum job Requirements: Master’s degree in Statistics, Math, Computer Science, Data Science, Operations Research or a related quantitative field. Must be proficient working with data in SQL and Python; Must understand multiple statistical data analysis and machine learning methods such as generalized linear regression, regression and classification trees, unsupervised learning methods, causal inference, stochastic processes, time series forecasting. May telecommute from any U.S. location.