Data Scientist

Demonstrated competency in developing production-ready models in an Object-Oriented Prog language such as Python. Demonstrated competency in using data-access technologies such as SQL, Spark, Databricks, etc. Experience with Visualization tools such as Tableau, Kepler.gl, etc. Experience with Data Modeling techniques such as Normalization, data quality and coverage assessment, attribute analysis, performance management, etc. Experience building machine learning models such as Regression, supervised learning, unsupervised learning, probabilistic inference, natural language modeling, etc. Excellent communication skills. Able to effectively lead meetings, to document work for reproduction, to write persuasively, to communicate proof-of-concepts, and to effectively take notes. Experience with Geospatial data search and analysis, geo-indexing techniques, vector and raster data structures. Experience with remote sensing, GIS tools, and satellite imagery analysis. Experience with CVML (Computer Vision & Machine Learning) Experience with advanced AI techniques and tools. Examples of professional work such as publications, patents, a portfolio of relevant project-work, etc. Familiarity with Distributed Datasets Experienced with a variety of data structures such as time-series, geo-tagged, text, structured, and unstructured. Additional experience with other languages such as Java, JavaScript, Scala, etc. Experience with simulations such as Monte Carlo simulation, Gibbs sampling, etc. Experience with model validation, measuring model bias, measuring model drift, etc. Experience collaborating with stakeholders from disciplines such as Product, Sales, Finance, etc. Ability to communicate complex analytical insights in a manner which is clearly understandable by nontechnical audiences.