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Machine Learning Engineer

Company: State Farm Mutual Automobile Insurance Company
Location: Dallas
Posted on: September 15, 2019

Job Description:

One Company.......Many Careers!

For over 95 years, data has been key to State Farm. As a member of our data science team, you will work across the organization to solve business problems and help achieve business strategies. You will employ sophisticated, statistical approaches and state of the art technology. You will build and refine our tools/techniques and engage with internal stakeholders across the organization to improve our products & services.

Implementing solutions is critical for success. You will do problem identification, solution proposal & presentation to a wide variety of management & technical audiences. This challenging career requires you to work on multiple concurrent projects in a community setting, developing yourself and others, and advancing data science both at State Farm and externally.

A Day in the Life:

  • Builds and supports machine learning model deployment solutions that provide value to the business
  • Optimize machine learning model for deployment, including hyper-parameter tuning automation, scalability, and distributed learning
  • Demonstrates up-to-date expertise in software engineering practices and applies this to the development, execution, and improvement of machine learning models
  • DevOps practices for continuous integration and continuous delivery of machine learning models
  • Work with a wide variety of models, including text analytics, natural language processing, and computer vision models
  • Build solutions that optimize data science and analytic workflows
  • Utilize best practices for code version control using Git
  • Assists in providing computer science related guidance
  • Develop and support strategic enhancements to machine learning operations that support data science
  • Participate in research to maximize code and model reusability
    We Are Looking for Candidates With These:

    Must Have Skills:
    • Python - Pandas, Numpy, Sipy, Flask, Jupyter notebooks
    • Modeling - Scikit-learn, tensorflow, h2o, pyspark
    • Batch, synchronous model deployment experience
    • Data preparation, feature engineering workflows
    • Software Engineering - Classes, functions, Pycharm, unit tests, Pylint
    • Git - Best practices
    • Continuous Integration / Continuous Delivery using Gitlab - CI
    • Containerization - Docker, Kubernetes
    • Linux
      Preferred/Desired Skills:
      • AWS
      • Hadoop Technologies
      • ML workflows - Kubeflow, MLflow
      • Asynchronous, near and real-time model deployment experience

        This position will have a 12-mo incumbency period for lateral move & 6-mo incumbency period for a promotional opportunity (beginning on effective date of the position) which must be met before posting.

        ***Multiple location opportunity:

        Dallas, Texas: Everything is big in Texas including opportunities at State Farm! Our CityLine Hub location offers easy access to DART, lots of parking and a wide variety of healthy dining options.

        Phoenix, Arizona: Phoenix is hot! Looking for a cool job? Our Marina Heights Hub is conveniently located on Tempe Town Lake in close proximity to Downtown Tempe. It can be accessed by car, bus, light rail, bike share and soon the Tempe street car!

        Bloomington, Illinois: Home to State Farm's Corporate Headquarters. Bloomington offers a fun, family-friendly, mid-sized community and boasts an average 15 minute rush hour commute. The community is very active and is home to two universities: Illinois State University and Illinois Wesleyan.

        Atlanta: Join us in Atlanta where southern hospitality combines with cutting-edge innovation. Our associates enjoy access to fully equipped gyms and cafes with healthy food options on location. Our location offers easy access to MARTA.




        PM18 PI113643520

Keywords: State Farm Mutual Automobile Insurance Company, Dallas , Machine Learning Engineer, Engineering , Dallas, Texas

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