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Must-haves:

10+ Years of Data Engineering/Data Science Experience developing and implementing ML models in Big data ecosystem i.e. Hadoop, Spark, HBase, Hive / Impala or any other similar distributed computing technology Proficiency (Expert Level) with Python and PySpark and basic libraries for machine learning such as scikit-learn, pandas Proficiency with DataIku or similar tools (Alteryx, Databricks, MatLab, Knime, AWS Machine Learning, Azure Machine Learning, Datarobot) Proficiency in data analysis using complex and optimized SQL and / or above-mentioned technologies

Plusses:

Cloud Experience w/Databricks, Snowflake, Azure, or AWS Team is eventually transitioning to Cloud Environment w/Databricks, Azure, Snowflake Financial Services Experience Experience directly working with business teams to gather requirements to build ML Algorithms Understanding of data structures, data modeling and software architecture Experience in architecture, design and implementation of data intensive applications for practical use-case

Responsibilities will include:

Developing and implementing ML models in Big data ecosystem i.e. Hadoop, Spark, HBase, Hive / Impala or any other similar distributed computing technology Diagnose and Fix Data Pipeline issues using advanced Python/PySpark and basic libraries for machine learning such as scikit-learn, pandas Conduct Data Analysis using Complex and Optimized SQL Queries