Description

Required Skill Sets:
Experience with scientific computing language and big data knowledge, including Python, SQL, Hive, Hadoop, Spark etc.
Experience with common machine learning algorithms (SVM, KNN, logistic regression, random forest, XGBoost, Neural Networks, etc.)
Develop and maintain ML/Stats models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.
Developed skills in the application of scientific methods to practical problems through exploratory data analysis, hypothesis testing and data visualization to reach robust conclusions.
Understanding of statistical probability distributions, bias, error and power as well as sampling and resampling methods.
Expertise in the manipulation, integration, processing and interrogation of large datasets. Maintain data quality and support data access.
Experience with source control tools such as GitHub and related CI/CD processes
Ability to tackle ambiguous and undefined problems and thrive with minimal oversight and process. 
Ability to communicate and discuss complex topics with technical and non-technical audiences

Education

Any Graduate