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Must-haves:
- 10+ Years of Data Engineering/Data Science Experience developing and implementingML modelsin 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