Data Scientist
Posted
- Demonstrated competency in developing production-ready code using Python & AWS infrastructure (e.g. S3, policies, etc).
- Proficiency in using data-access technologies such as SQL, Spark, Databricks, MongoDB.
- Experience with data storage and management of various types of data (e.g. images, rasters, parquet, time-series, geo-tagged, text, and other unstructured data).
- Familiarity with spatial visualization tools (e.g. Folium, Plotly), and labeling systems like SuperAnnotate, Labelbox, Voxel51.
- Experience in developing alerts (e.g. through email automation), dashboards (e.g. Tableau), or other means to monitor system performance and issues.
- Excellent communication skills, including the ability to lead meetings, document work for reproduction, write persuasively, communicate proof-of-concepts, and effectively take notes.
- Capacity to communicate complex analytical insights in a manner understandable by non-technical audiences.
- Experience developing plugins with other programming languages such JavaScript.
- Knowledge of model validation, measuring model bias, and measuring model drift.
