DISH Network L.L.C.

  • Data Science Lead

    Location US-CO-Englewood | US-UT-American Fork
    Job ID
    2019-47506
    Category
    Engineering
  • Summary

    Sling TV L.L.C. provides an over-the-top (internet delivered) television experience on TVs, tablets, gaming consoles, computers, smartphones, smart TVs and other streaming devices. Distributed across a variety of strategic device partners, including Google, Amazon, Apple TV, Microsoft, Roku, Samsung, LG, Comcast, and many others, Sling TV offers two primary domestic streaming services that collectively include more than 100 channels of top content. Featured programmers include Disney/ESPN, NBC, AMC, A&E, EPIX, NFL Network, NBA TV, NHL Networks, Pac-12 Networks, Hallmark, Viacom, and more. For Spanish-speaking customers, Sling Latino offers a suite of standalone and extra Spanish-programming packages tailored to the U S. Hispanic market.  And for those seeking International content, Sling International currently provides more than 300 channels in 20 languages (available across multiple devices) to U.S. households. 

     

    Sling TV is the #1 Live TV Streaming Service  Sling TV is a next-generation service that meets the entertainment needs of today’s contemporary viewers. Visit www.Sling.com. We are driven by curiosity, pride, adventure, and a desire to win – it’s in our DNA. We’re looking for people with boundless energy, intelligence, and an overwhelming need to achieve to join our team as we embark on the next chapter of our story.

     

    Opportunity is here. We are Sling.

    Job Duties and Responsibilities

    About the position

    Our mission is to build the next generation, web scale platform for Sling TV.  Our environment is…

     

    • Complex
    • Highly elastic
    • Based on some of the latest and greatest cloud native technologies
    • Very fast paced

    Your team will be…

     

    • Enabling a proper enterprise Data Lake in AWS
    • Building models and tools to get value out of the mass amounts of data we have in our environment
    • Enabling the best, most personalized and resilient customer experience possible

    In order to be successful in this role, you will need to be…

     

    • Highly motivated, driven & hard working
    • Able to lead a team of 3-5 into the world of data science
    • Not afraid to fail and comfortable working independently and with a team
    • Comfortable working with massive datasets in real time and batch processing with superior analytics skills
    • Comfortable talking to and working with Senior Executives
    • Apply data mining techniques, do statistical analysis, and build high quality prediction systems integrated with our product. Doing ad-hoc analysis and presenting results in a clear manner.
    • Processing, cleansing, and verifying the integrity of data used for analysis
    • Enhancing data collection procedures to include information that is relevant for building analytic systems
    • Data mining using state-of-the-art methods. Create automated anomaly detection systems and constant tracking of its performance.
    • A team player. We have a great group of diverse folks working together in harmony.  Big egos and “super heroes” need not apply.
    • Real-time machine learning

    Skills - Experience and Requirements

    Basic Requirements:

    A successful Data Science Lead will:

    • Be available to work onsite out of our American Fork, UT or Englewood, CO offices
    • Have a 4-year college degree in Computer Science / Information Technology, master’s degree and or a PHd is preferred or equivalent professional experience
    • 10+ years professional enterprise experience
    • Open to occasional travel for quarterly planning meetings and or other key workshops

    Here are some of the key technologies that make up our environment.  While we do not expect you to have a detailed understanding of each, the more of these you are familiar with the better.

     

    • ELK Stack / HDFS / Hadoop / Hive
    • Linux
    • AWS Big Data Tools: S3, Kinesis, Red Shift, Athena
    • Java, Python, R, SQL
    • Machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, h20.ai, etc.
    • Artificial Intelligence
    • Predictive Analytics
    • Tableau or other visualization tools
    • Kafka / Confluent
    • CI / CD and Cloud Native Computing (Docker, Kubernetes, Consul, Vault)

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