Company Summary

DISH, an EchoStar company, has been reimagining the future of connectivity for more than 40 years. Our business reach spans satellite television service, live-streaming and on-demand programming, smart home installation services, mobile plans and products and now we are building America's First Smart Network™.

Today, our brands include EchoStar, Hughes, DISH TV, Sling TV, Boost Mobile and Gen Mobile.

Department Summary

Our Technology teams challenge the status quo and reimagine capabilities across industries. Whether through research and development, technology innovation or solution engineering, our people play vital roles in connecting consumers with the products and platforms of tomorrow.

• Understand business requirements and priorities and work with stakeholders to identify high-impact, feasible roadmap areas. Collaborate with business teams and help them understand how data science methods can positively impact the business
• Develop analyses and models such as customer segmentation, user modeling, churn analysis and prediction, and lifetime value prediction
• Process large volumes of customer and device data to gain actionable insights and make data-driven decisions
• Collaborate with other team members, including data scientists, engineers, and quality analysts, to help the team progress and achieve results. Provide technical guidance to other team members as needed
• Help multiple audiences - engineers, product managers, executives - understand the analyses and models using notebooks (e.g. Rmd, Jupyter), visualizations, and presentations
• Identify and adopt new techniques and tools to stay at the forefront of modern large-scale data processing, analysis, and machine learning capabilities

• Strong academic background in statistics and machine learning - a Bachelor's or Master's degree in Math, Statistics, Computer Science, Physics or such quantitative fields or has done a program from a business school in marketing, analytics etc., with a focus on quantitative approaches
• 4+ years of experience related to data and data analysis. You have worked on a variety of complex data analysis and modeling problems and applied these techniques to real-world scenarios

• Proficiency with statistical and machine-learning tools: linear models for regression and classification, multilevel models, factor analysis & PCA, discriminant analysis, support vector machine, decision tree ensembles & bootstrap, neural networks, mixture models & clustering algorithms, etc.
• Proficient in a programming language commonly used for data analysis (Python), and comfortable with SQL
• Experience with business analytics problems: customer churn, lifetime value estimation, targeted marketing, personalized offers, etc.
• Experience in designing and analyzing controlled experiments for targeted interventions

• Masters or Doctorate in relevant domain
• Experience with database and data warehousing systems such as Amazon Athena and Redshift, BigQuery, and Teradata
• Experience working with large data sets and big data processing tools such as Spark
• Data engineering skills to do preprocessing, cleaning and transformations