Position Description:

***Applicants are permitted to work remotely from an at-home work site anywhere in the United States.***

Consults with business and technology partners to identify priorities and establish data analytic goals. Translates use cases to Artificial Intelligence (AI), Machine Learning (ML), and analytics — data, algorithms and validation strategy. Drives data identification, collection, and qualification activities. Leads efforts to identify signals in data that address use cases directly or can be leveraged by analytics teams. Designs and provides critical reviews for algorithmic approaches.

Primary Responsibilities:

• Provides ML leadership on complex projects, often across several business units and functions.

• Works directly with technology teams to integrate data science models into production systems.

• Writes and delivers reports on findings for technical and non-technical audiences.

• Provides critical reviews for algorithm designs.

• Provides core reviews for algorithm implementations.

• Conducts research and publications for the technical community.

• Assesses data and algorithm design recommendations from data scientists and recommends changes to larger and more complex systems.

• Defines data and model governance practices to operationalize standards.

• 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.

• Manages the formulation of mathematical modeling and optimizing methods to develop and interpret information that assists management with decision making, policy formulation, or other managerial functions.

• Collects and analyzes data and develops decision support software, service, or products.

• Develops and supplies optimal time, cost, or logistics networks for program evaluation, review, or implementation.

• Elicits, captures, and interprets customer problems from multiple perspectives across projects or within a program.

• Establishes and project manages analysis plans for multiple complex work steams.

• Manages to the estimation, planning, analysis, design, and development of projects.

• Holds accountability for integration into larger, multi-disciplined projects, as appropriate.

• Leads and oversees ML strategy and road map planning.

• Works across teams to influence and lead the direction of external teams.

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Network Science, Analytics, Data Science, Advanced Computer Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, or a closely related field and five (5) years of experience as a Senior Manager, Data Science (or closely related occupation) researching and building scalable AI solutions using ML and Natural Language Processing (NLP) models and technologies to improve customer experience and drive business results.

Or, alternatively, Master’s degree (or foreign education equivalent) in Network Science, Analytics, Data Science, Advanced Computer Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, or a closely related field and two (2) years of experience as a Senior Manager, Data Science (or closely related occupation) researching and building scalable AI solutions using ML and Natural Language Processing (NLP) models and technologies to improve customer experience and drive business results.

Or, alternatively, PhD degree (or foreign education equivalent) in Network Science, Analytics, Data Science, Advanced Computer Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, or a closely related field and no experience.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) performing advanced statistical analytics to develop and evaluate supervised and unsupervised ML algorithms — Regression, Decision Trees, Neural Networks, Feature Selection, Hyper-Parameter tuning, and ranking models — using Python or ML libraries (scikit-learn, Tensorflow, or PyTorch).

• DE designing and developing NLP solutions to process unstructured and semi-structured text for NLP tasks – Named Entity Extraction (NER), classification, or clustering – using classical NLP or ML methods (Deep Learning (DL) and embeddings); launching ML and DL models in a production environment; and performing A/B testing to assess the efficacy of the ML and AI algorithms.

• DE collecting, processing and analyzing large scale datasets using Python, Pandas, or Spark to preprocess, clean, analyze and visualize both unstructured and structured data.

• DE refactoring production-level code to achieve greater run-time performance and low latency using parallel processing; and prototyping and deploying ML solutions using Cloud-based environments (Google Cloud, Amazon Web Services (AWS) servers or AWS SageMaker).

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