Reporting on productivity and project progress and ensuring that they are compliant with quality standards.
Using Databricks and SQL for reporting and analytics, to write queries to answer questions and perform ETL tasks to create datasets.
Maintaining both internal and external channels of communication.
Utilizing Python libraries (scikit-learn, pandas, numpy) to conduct statistical analyses.
Gathering details regarding the business of the operations area by using a variety of methods (shadowing interviews, shadowing, surveys or reading reports, etc.).
Performing statistical tests such as k-means, OLS and MLS regressions, and logistic regressions.
Working with stakeholders to scope and plan projects and analysis topics.
Providing findings and data driven recommendations to leadership.
Writing tests and logging for data pipelines and automation.
