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+ year’s experience in cloud data engineering experience, with couple of engagements in AWS
5+ years strong experience in SQL, stored procedures, ETL in Snowflake Environment.
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.