Lead Python Application Developer

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.