Machine Learning Architect

About Asurion

For two decades, Asurion has led the technology protection industry around the globe. The Company provides premier support solutions to enable optimum use of technology; digital applications to protect their privacy and provide security; and rapid replacement of lost, stolen, damaged or malfunctioning devices. Asurion partners with the leading wireless companies, retailers and service providers enabling them to focus on their businesses and to provide services that delight their customers. Asurion's 16,000 employees worldwide specialize in fulfilling the needs of more than 280 million consumers.

We value open source technologies, solve challenging and unique problems, and innovate quickly. We embrace continuous delivery and Lean Startup principles. We encourage creativity from our architects and engineers every step of the way, working with various teams including product, user experience, call center operations, mobile and systems. Our teams are small enough to make fast decisions, yet our audience is large enough that our work makes a tremendous impact.

Job Description

Asurion is seeking a Principal Machine Learning Architect to drive Asurion's machine learning strategy and its platform architecture. This position reports to the Director of Enterprise Data Architecture and Analytics Strategy. This position will be responsible for building Machine Learning architecture and solutions to maximize the interpretation of our data in order to provide reliable predictive models. You will join a growing multi –disciplinary team of Data Scientist, Engineers and Solution experts with deep domain knowledge who are working to develop new capabilities and workflows to solve real-world problems.

We're looking for someone who is passionate about data and analytics, has a love for solving hard business problems, and enjoys learning about new technology. If this sounds like you, get in touch!

The principal architect will help lead Asurion in defining the strategies, roadmaps and solutions to harness the value of data and analytics. This role is critical in leading the organizational transformation in the cloud data platforms and advance analytics domains. The role will:

  • Define the strategy & roadmap for analytics \ deep learning frameworks and evangelize the vision to the organization.
  • Design, document and lead the implementation of software and systems to help ensure optimal implementation of the neural network models, real-time analytics with enterprise data.
  • Design, develop and implement Machine Learning analytics technology stack for prescriptive & predictive analytics.
  • Work in conjunction with Infrastructure, Dev Ops, Security and Engineering groups to drive the strategy to provide stable, secure and enterprise-class data-lake and real-time analytics systems.
  • Design highly-available data lake architectures, utilizing IaaS and PaaS, for large scale, mission critical applications and high-performance-computing (HPC) workloads.
  • Provide guidance to the organization in the form of reference architectures, guidance principles, and proof of concept and reference implementations for cloud based data platforms and advance analytics tools and technologies.
  • Work closely with project architects to align the projects' architectural direction with domain roadmaps, standards and reference architectures.
  • Work with cross-functional departments and delivery teams to ensure that they understand the prescribed direction. Oversee the execution of key architectural initiatives within this domain.
  • Hands-on in the delivery of enterprise data-lake on AWS and the required solutions to meet enterprise analytic and business intelligence needs.
  • Lead design and implementation new capabilities in support of Provide guidance to other developers in the technologies being assessed and used. Assist Management with the training and mentoring of the team members.
  • Collaborate with other teams to address upstream and downstream integration dependencies via services and SLA's
  • Demonstrate technical leadership and ability to contribute to establishing the overall solution direction.
  • Be self-starters with the initiative and enthusiasm to learn new tools & technologies within the Hadoop eco system and AWS platform in a quick paced ever-changing environment.
  • Maintain a working knowledge of Asurion's applications and system integration..
  • Lead presenter in Architecture Review Board (ARB) Process
  • Evaluate and/or lead Product Evaluation Matrix (PEM) development

Our ideal candidate will have a both a software engineer background combined with a machine learning background along with strong work ethic, fantastic attitude and be comfortable tackling any challenge set before him or her. We provide significant flexibility and autonomy to team members, have high expectations and expect everyone to contribute meaningfully to our broader collective goals.

Minimum Qualifications

  • MS degree in Computer Science or related quantitative field with 5 years of relevant experience or Ph.D degree in Computer Science or related quantitative field
  • Experience in one or more of the following areas: machine learning, large-scale data mining or artificial intelligence.
  • Proven ability to translate insights into business recommendations
  • Experience with distributed computing frameworks Yarn, kubernetes, AWS ECS
  • Experience with Docker, Orchestration
  • Experience with ML frameworks – TensorFlow, Caffe2, MxNet, H20, PredictionIO, CNN, RNN, Torch, Java, Scala, Python, R, CUDA, OpenCL
  • Experience developing and debugging in Java/Scala/R/Python, Knowledge of functional programming
  • Experience with Spark ML/Hadoop is a plus
  • Experiences with AWS infrastructure is a plus

Use Cases

Some of the use cases this team member will work on are time series forecasting, clustering, classification, natural language processing, and customer segmentation, including others. We contribute to the technology behind data normalization, text analytics, recommendation systems, and machine learning with the goal of making enterprise data actionable.


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