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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