Principal Data Scientist
Our client builds and manages the foundational technologies on which the tech organization is built – including cloud, data, enterprise architecture, DevOps, infrastructure, and security. They are also responsible for building a world class enterprise services organization, including service operations and technology solutions for our 2.2 million associates worldwide.
§ Sunnyvale, California
§ Bentonville, Arkansas
Data Scientist Lead uses advanced techniques that integrate traditional and non-traditional datasets to enable analytical solutions; Applies predictive analytics, machine learning, simulation, and optimization techniques to generate management insights and enable customer-facing applications; Builds analytical solutions leveraging internal and external applications to deliver value and create competitive advantage; Leads initiatives and influences system architecture to enable automated intelligent solutions; Translates complex analytical and technical concepts to senior management to enable business decisions; Maintains relationships with academic and industry thought-leaders to ensure enterprise has access to cutting-edge thinking and technologies.
Partners with business leaders across the organization to assess business needs, define business problems and develop a research agenda
Leads cross functional, matrixed teams to solve highly complex work critical to the organization.
Designs and builds large and complex information sets
Integrates and extracts relevant information from large amounts of both structured and unstructured data (internal and external) to enable analytical solutions.
Leads and conducts advanced analytics leveraging predictive modeling, machine learning, simulation, optimization and other techniques to deliver insights or develop analytical solutions to achieve business objectives.
Leads efforts to develop scalable, efficient, automated solutions for large scale data analyses, model development, model validation and model implementation.
Works with IT to translate prototypes into new products, services, and features and provide guidelines for large-scale implementation.
Builds and maintains a robust library of reusable algorithms and supporting code such that research efforts are based on the highest quality data, are transparently conducted, are able to be productionized and are reproducible.
Provides guidance regarding analytical approach and iteration of algorithms to team members.
Translates complex analytical and technical concepts to senior management and non-technical employees to enable understanding and drive informed business decisions.
Develops and maintains academic and industry relationships for the purposes of research; Interacts with internal and external peers and management to maintain expertise and awareness of cutting edge techniques, technologies and potential business solutions.
Master’s degree in Computer Science, Applied Mathematics, Quantitative Economics, Statistics, or related field (6 additional years of related experience beyond the minimum required may be substituted in lieu of a degree)
8 or more Years in predictive modeling, large data analysis and computer science
Experience in stochastic modeling, machine learning, and other advanced mathematical techniques (e.g., neural nets, simulation, graph analysis)
Expert in at least one compiled language (e.g., Java, C or more or more) and one dynamic scripting language (Python, PERL, Ruby)
A strong track record of communicating results, insights, and technical solutions to Senior Executive Management (or equivalent)
Prior experience using Speech-to-Text and/or Natural Language Processing (NLP) algorithms to develop models using unstructured text that were successfully implemented or used in a production environment; experience with the Gridspace Sift tool a plus.
Highly competent at data engineering in SQL and/or SAS as well as advanced machine learning (ML) techniques using Python; comfortable in cloud computing environments (Azure, GCP, AWS).
Expertise in using advanced statistical analysis to discover key relationships in data and applying that information to predict likely future outcomes.
Proven ability to enrich (add new information to) data, advise on appropriate course(s) of action to take based on results, summarize complex technical analysis for non-technical executive audiences, succinctly present visualizations of high dimensional data, and explain and justify results of work conducted.
Fluent in deep learning frameworks and libraries (TensorFlow, Keras, PyTorch, etc).
Hands-on experience delivering products or solutions that utilized deep learning neural networks in areas such as computer vision, Natural Language Processing (NLP), sensor data from the Internet of Things (IoT), and recommended systems.
Highly skilled in handling Big Data (Spark, Kafka, etc).
Experience in publishing at top ML, computer vision, NLP, or AI conferences and/or contributing to ML/AI-related open source projects and/or converting ML/AI papers into code is a plus.
PhD in Computer Science, Applied Mathematics, Quantitative Economics, Operations Research, Statistics, or related field with coursework in advanced Machine Learning techniques (Natural Language Processing, Deep Neural Networks, etc).
- The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job.
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