Manager, Data Science

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  Designs, develops, and implements AI/ML mathematical models for production business use cases.

• Assesses and evaluates AI models for fairness, bias and exploitability.

• Identifies and ingests new data sources and performs feature engineering for integration into AI models.

• Structures work, plans new analyses, and translates business questions into analytical projects and AI-powered capabilities.

• Manages and delivers complex/critical AI/ML projects and discovers and verifies new opportunities to identify risk, grow business, and scale and optimize operations.

• Works across technologies and business functions to develop solutions at all levels of a data science stack, from data engineering and sources to production.

• Develops and applies mathematical or statistical theory and methods to collect, organize, collect, and analyze data and develop decision support software, service, and products.

• Formulates mathematical and simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.

• Performs validation and testing of models to ensure adequacy and reformulate models as necessary.

• Defines data requirements and gathers and validates information, applies judgment and statistical tests.

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  Demonstrated Expertise (“DE”) designing, testing, and implementing Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) models, using advanced modeling approaches (BERT, RoBERTA, and auto-encoders/decoders), and Natural Language Understanding and Generation (NLU/NLG); and building computer vision algorithms, using advanced technologies requiring high performance computer environment (PyTorch and TensorFlow).

• DE serving ML and DL models in low-latency production systems, using REST API endpoints; performing source code management and version control, using Git; and integrating DevOps tools for automated deployments to build and promote Continuous Integration and Continuous Deployment (CI/CD) practices.

• DE building and maintaining data pipelines to deploy and run AI and ML models to generate personalized Next Best Actions and Next Best Contents recommendations, using Amazon Web Services (AWS) Sagemaker.

• DE researching and creating algorithms, software, and web applications to visualize and surface AI and ML models into production.

[Expertise may be gained during graduate degree program]