Lead Python Application Developer - Global Channel Management, Inc

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.