Lead Python Application Developer

4 weeks ago

Iselin, United States Global Channel Management Full time

Lead Python Application Developer needs 7+ years overall experience with 1-3 years in an ESG technology focused role within asset management or financial services industry.
Lead Python Application Developer requires:
•Iselin, NJ ( Hybrid) 3 days a week
•Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas.
•Experience with machine learning tools such as scikit-learn, R, Theano, TensorFlow, SparkML, or Foundry
•Experience in using two or more of the following modeling types to solve business problems: classification, regression, time series, clustering, text analytics, survival, association, optimization, reinforcement learning.
•Understanding of data models, large datasets, business/technical requirements, BI tools, statistical programming languages and libraries
•Demonstrates functional knowledge of data visualization libraries such as matplotlib or ggplot2; knowledge of other visualization tools such as Microsoft Power BI , Quick Sight or Tableau .
•Knowledge of cloud & computing technologies such as: Hadoop, Apache Spark, AWS, Microsoft Azure or Google cloud.
•Bachelor's or Masters degree in computer science, data science, statistics, mathematics, or a related field.

Lead Python Application Developer needs
• Create and maintain data flow design and technical requirements documentation using defined documentation templates that meets Agile product development standards (such as data analysis or methodology, MS Excel calculations).
• Understand business objectives and develope models that help to achieve them, along with metrics to track their progress
• Demonstrating the results of various algorithmic approaches and evaluating their performance
• Leverage a broad set of modern technologies - including Python, R, Scala, and Spark - to analyze and gain insights within large data sets and implement systems for automatic data collection, curation and model training
• Analyze diverse sources of data, extract features from data sources, train and test models, and Productionalize the models that significantly improve business outcomes.
• Works closely with Data Scientists and Data Engineers to develop predictive algorithms
• Training models and tuning their hyperparameters.
•

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