RESPONSIBILITIES:

• Design, develop, and deploy AI/ML models using state of the art techniques available in the open stack (Python/PySpark/PyTorch) and/or vendor solutions
• Partner with LOB leads to frame the problem, explore various ML/DL model architectures and methodologies, generate required artifacts related to model development life cycle (MDLC), author the model development document, and deliver AI models that meet business needs
• Adhere to corporate model risk policy and ensure compliance with model risk management
• Working with other data science teams to identify, gather, retain, and publicize modeling artifacts required for approved and repeatable processes
• Work with AI technology and production teams to operationalize models
• Work effectively in an agile project management methodologies for data science
• Knowledge sharing with members of the team and across the organization on topics including machine learning algorithms, hyper-parameter tuning/search, and traversing across multiple big data platforms
• Contribute to the CoE data science team’s group effort to stay current with the cutting edge NLP/ML/DL algorithms, methodologies in the open source community and vendor solutions.

Required Qualifications

• 4+ years of experience in an advanced scientific or mathematical field
• A master’s degree or higher in a quantitative field such as mathematics, statistics, engineering, physics, economics, or computer science
• 4+ years of statistical modeling experience
• 3+ years of Python experience
• 2 + years of experience using quantitative machine learning techniques

Desired Qualifications

• 2+ years of Hadoop experience
• 1+ years of Natural Language Processing (NLP) experience

Other Desired Qualifications

• Hands on familiarity with machine learning and statistical modeling techniques applying machine learning techniques such as neural networks, random forest, GBM and SVM, Probabilistic Graphical Models, Deep Learning architectures, using open-source languages like Python, PySpark and/or PyTorch.
• Experience with model monitoring and performance tracking
• Hands on experience with deep learning toolkits such as Tensorflow, Keras, PyTorch, Dynet
• Hands on experience writing data processing and data pipeline for model development including gathering and building datasets to collect intents, joining and aggregating source datasets, cleaning messy data, designing feedback loop on data needs
• Experience building intent recognition and classification models. Experience with phrase level identification. Experience with language models such as BERT, ELMO, CRF
• Experience with active learning and reinforcement learning
• Experience with AI model transparency and explainability studies
• Strong acumen in diagnosing and resolving data issues
• Exceptional analytical, critical thinking, quantitative reasoning skills, and problem-solving skills.