Experience-12-15 years
Job Description:
· 12+ years of total experience in data analysis, data science development and 8 years in (AWS) Sagemaker /Python/UDF experience. Insurance analytics knowledge will be plus.
· 5+ years experience in cloud data engineering experience, with couple of engagements in AWS
· 5+ years strong experience in SQL, stored procedures, ELT in Snowflake Environement.
· 1+ years of experience in Python development for Data engineering.
· Experienced in data science / machine learning lifecycle processes, methods, tools.
· Medium to strong statistical understanding, ability to understand and infer model reports, feature importance, validation reports and advise domain/business SMEs and help to make right decisions on the modelling
· Hands on programming experience in R for Data analysis, Statistical inferencing and data science modelling in R.
· Experience in Hyper parameter tuning, model validation & evaluation frameworks and tools in R , Hyper parameter tuning,
· Knowledge of other tools for ML, MLOps, Data Prep and languages (Python) will be a plus
· Strong scripting knowledge in R & SQL
· Experience cloud/on-prem devops tools for orchestration, scheduling, logging, required for data engineering development.
· Exposure/experience in Data tools in Snowflake & AWS Eco system and & DataOps tools on AWS
· Understand the use cases, create specifications. Design the pipelines
· Build and test ETL/ELT components in Snowflake native, Qlik Replicate / AWS Glue or similar services on AWS.
· Exposure/experience in Data tools in Snowflake & AWS Eco system and & DataOps tools on AWS
· Familiar with data engineering Familiar with data science / machine learning lifecycle processes is essential.