Data Science - Data Analytics Support

The Data Scientist Level-1 (ML/AI/Data Engineering) is responsible for identifying relevant data sources, collecting and organizing structured and unstructured data, building predictive models and machine learning algorithms, analyzing data for trends and patterns, and generating insights and reports. Further Data Scientist Level-1 is responsible for 
• Processing, cleansing & verifying of data 
• Analyzing data for trends and patterns and to find answers to specific questions 
• Setting up data infrastructure, data catalogs 
• Develop, implement, and maintain databases for features, machine learning algorithms 
• Assess quality of data and remove or clean data 
• Generating information and insights from data sets and identifying trends and patterns 
• Preparing reports for executive and project teams 
• Create and manage visualization of data 
• Mapping data sources: An understanding of where data is stored and maintained 
• Defining logical integrative views of data: These views will draw together data from across the domains 
• Some views will use a database of extracted data - others will bring together data in "near real time." 
• Documenting existing data movement: record how data movement is happening

Required:
• Strong mathematical & numeracy skills 
• Intermediate understanding of databases such as SQL Server, Informix, and other Cloud solutions such as PostgreSQL, Redshift, Cloudera 
• Understanding of reporting & data visualization tools such as OBIEE and Tableau. 
• Impala - Understanding of website scripts such as XML, JavaScript, JSON 
• Understanding of ETL framework and ETL tools including DataStage, Glue ETL 
• Proficiency in Python and other Machine learning services such as Amazon SageMaker 
• Knowledge of Geospatial analytics 
• Excellent analytical skills - the ability to identify trends, patterns, and insights from data FPAC Data Analytics Support - Performance Work Statement (PWS) 19

Preferred:
Certification in data science or related areas.
Experience with additional databases, cloud platforms, and virtualization technologies.
Familiarity with containerization and orchestration tools like Docker and Kubernetes.
Experience in testing web services and APIs. 
Understanding of DevOps principles and practices.