Assist with feasibility studies to identify data availability, quality and modeling requirements and dependencies
Support modern data storage, movement, and transformation architectures and techniques to extract and engineer features from any scale structured or unstructured data in health, IT system, business process, or external data
Contribute to discovery or engineering of explanatory features in high-dimensionality collections of data that relate to clinically, financially, and/or operationally important use-cases using scientifically valid techniques
Support iterative selection and application of modern statistical and machine learning techniques and evaluation methods to engineered features to derive a best candidate model
Interact with business teams and leaders to identify relevant questions and issues for data analysis and experimentation that support business needs or problems.
Propose new uses for existing data sets or sources, algorithms and predictive models
Requirements
Languages: Python, Java, SQL
Building Dashboards such as Grafana, OpenSearch, Oracle Analytics Cloud
Cloud Development such as (AWS, Microsoft Azure, Google Cloud, OCI Cloud) and building ETL Pipelines