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Senior Machine Learning Engineer at Domino's Pizza

As a leader of innovation in the food and digital commerce space, Domino’s is constantly testing new concepts, platforms, and technologies that drive outstanding consumer and employee experiences, which requires a disciplined data approach. The primary responsibility of this role will be to develop the machine learning and AI components of Domino’s next-generation smart applications. This role will be required to architect solutions that are manageable and supportable in high-volume, low-latency production environments and will support the team responsible for building, deploying, and maintaining predictive models for different areas of the business. In addition, this role provides architecture consulting and devops support for the broader Strategy and Insights group enabling data science teams to integrate with ecommerce and retail technology platforms.

RESPONSIBILITIES AND DUTIES

(60%) Architect solutions to solve problems using machine learning

  • Improve and augment employee and consumer experience, store operations, and process optimization through ML/AI solutions
  • Leverage data and requirements gathered from business partners to build machine learning applications
  • Quickly prototype new algorithms to demonstrate value
  • Work with internal clients and the Data Science team to solve problems
  • Construct meaningful stats and machine learning datasets to answer relevant questions and enrich internal resources
  • Learn aspects of Store Operations, Delivery, and E-Commerce in the Quick-Service Restaurant industry for productive consulting

(30%) Provide guidance and support / expand use of ML

  • Coach team members less experienced with machine learning methodology
  • Consult with various data science team to architecture solutions, optimize software development, and deploy solutions into production environments.
  • Work as a bridge between data science and IT groups to support integration of AI/ML modeling into production environments.

(10%) Analytics and Statistical Analysis

  • Create and execute test plans that help address questions from non-technical business partners; translate results into business recommendations or interpretations
  • Consult inter-departmentally on new product deployments and incremental improvements
  • Consult on data collection for new products and specify requirements to ensure all tracking is in place for future analysis
  • Conduct analysis by mining internal and external data sources

Qualifications:


  • 5+ years of industry experience
  • Master’s degree (or Bachelor’s degree with equivalent experience) in a quantitative science such as statistics, mathematics, computer science, engineering, etc.
  • Experience with Unix
  • Experience with Docker
  • Version control, software experience, Object oriented coding experience
  • Experience with streaming data and real-time inference
  • Comfortable creating and using APIs and integration into production environments
  • Experience scaling a model to production load and monitoring
  • Intermediate or Advanced Skills with at least one scripting and/or programming language, e.g. Python, R
  • Intermediate or Advanced skills with various machine learning techniques (clustering, classification, forecasting, etc.)
  • Proficient with data integration and ETL (Pandas, SQL, etc.)
  • Proficient in applying statistical concepts
  • Ability to self-start and self-direct work in a fast-paced environment
  • Ability to rapidly learn how different areas of a business operate
  • Ability to effectively communicate results of a complex analysis with a diverse non-technical audience
*Please submit a 1-2 page resume for consideration*

The Following are a plus but not required:

  • Experience training models with at least one deep learning (neural networks) framework (TensorFlow, CNTK, Torch, Caffe, etc.)
  • 1 year experience with visualization software (PowerBI, Tableau, Matplotlib, ggplot2, etc.)
  • Experience with effective visualization techniques
  • Experience working with Big Data technologies such as HDFS, Hive, Spark, S3 storage, etc.

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