Permanent General Companies, Inc.

  • Machine Learning Engineer

    Location US-TN-Nashville
    Posted Date 1 week ago(1 week ago)
    # Positions
    1
    Category
    Computer and Mathematics - Data Scientist
  • Overview

    The General® Insurance, also known as Permanent General Companies, Inc., is a growing company and a leading non-standard automobile insurance provider. We are currently seeking qualified applicants for Machine Learning Engineer. We offer great training, a well defined career path and a fun and challenging work environment where the right candidate will learn and grow with the company.

     

    We pride ourselves on teamwork and quality customer service. If you do too, please check us out!

     

    The General® offers a generous benefits package to its employees including medical, dental, vision and life insurance after one month of employment; health care and dependent care flexible spending accounts, tuition reimbursement, paid time off (vacation, sick, holidays), 401(k) participation with a matching contribution,wellness initiatives and much more!

    Responsibilities

    The Machine Learning Engineer will support The General® in the disciplines of Data Science to institute an industry leading Center of Excellence focused on actionable business insights. Combine business acumen, analytical problem solving skills, and programming knowledge to discover and identify new opportunities through multiple forms of analyses using vast amounts of data. Create and deliver curated data experiences to inform business decisions and place the power of new and existing data sources into the hands of our business leaders and decision makers, enabling dominance in the Non-Standard automobile insurance market.

     

    Essential Functions

     

    A successful Machine Learning Engineer will working closely with the Data Scientists on the Data Science team to:

    • Productionalize and deploy models within a scalable solution to solve actionable, real-world problems with self-monitoring capabilities.
    • Improve efficiency of existing model training pipelines or create new ones for automated model re-training and deployment at scale.
    • Deploy robust, highly available decisioning and alerting pipelines.
    • Gather evidence through research, and apply advanced mathematical and statistical analysis in problem solving.
    • Quickly learn about new data, and advise on most efficient ways to productionalize various types and forms of models.
    • Stay current on innovative modeling approaches.

    In addition, the Machine Learning Engineer will:

    • Utilize technical and industry knowledge to drive multiple data projects across different analytics platforms & data domains.
    • Have deep technical skills in data engineering, statistics, machine learning, or deep learning and a passion for making these methods more rigorous, robust, and scalable.
    • Have practical experience building models on large datasets and turning prototypes into production models in one or more domains.
    • Be responsible for creating the pipeline infrastructure to productionalize various API calls.
    • Build Data Science products across a wide range of digital data streams (Anomaly Detection, Deep Learning, Information Retrieval, etc.).
    • Preferably can demonstrate hands-on experience with text mining and NLP approaches.
    • Use code repositories to version and share code/notebooks.
    • Regularly attend key initiative standups, proactively advising on opportunities to apply and the best approach to apply underlying empirically-developed algorithms.
    • Participate in code reviews with other engineers and regular meetings with the DevOps team

    Qualifications

    Knowledge, Skills and Abilities (KSAs)

    • Exceptional time management and organizational skills, quantitative thinker. 
    • Ability to clearly communicate technical concepts to a non-technical audience through compelling visuals and high quality presentations, while providing advice and recommendations. 
    • Adept at building and maintaining relationships. 
    • Assists in ensuring data integrity and quality are maintained throughout the organization.
    • Modeling exposure in R or Python. 
    • Comfortable consuming network-based APIs, preferably REST/JSON.
    • Hadoop ecosystem experience preferred, specifically Apache Spark. 
    • Test-driven development experience preferred. 
    • Docker / Kubernetes exposure preferred. 
    • Understanding of microservices architecture design and implementation preferred.
    • Exposure with various machine learning libraries including: caret, scikit learn, h2o.ai, tensorflow, pytorch, Spark ML, DL4J.
    • Basic understanding of and exposure to various modeling approaches including: GLMs, decision trees, clustering techniques and neural networks, naïve bayes, etc.
    • Familiarity with Business Intelligence tools such as Alteryx or Tableau, DataRobot. 
    • Advanced knowledge of Microsoft Office expected. 

    Education and Experience

    • Bachelor’s in Machine Learning, Statistics, Computer Science, Information Management, Physics, or related field (Master’s preferred). Related work experience will be considered.
    • Experience in Linux/Unix preferred.
    • Experience developing and managing machine learning based applications preferred.
    • NoSQL database experience is desirable (e.g. DynamoDB, MongoDB, Redis).
    • Experience with Amazon Web Services (preferably including EC2, EMR, S3, ECS, Lambda, DynamoDB, SageMaker, and Snowflake). 
    • Strong programming experience in at least one of: Python, Java, C++, Scala, or other object-oriented programming language.
    • Experience developing and managing RESTful API applications to expose machine learning models preferred. 
    • Strong SQL experience (preferably including database design, stored procedures, and query optimization).
    • ETL experience.

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