Data Scientist - Customer Lifecycle

Company Summary

Echo Star is reimagining the future of connectivity. Our business reach spans satellite television service, live-streaming and on-demand programming, smart home installation services, mobile plans and products. Today, our brands include Boost Mobile, DISH TV, Gen Mobile, Hughes and Sling TV.

Our Retail Wireless team, serving our Boost Mobile and Gen Mobile brands, is redefining consumer expectations through new platforms, new business models and new ways of thinking. Equipped with a passion for change and the power to drive it, we continue to push boundaries and be a disruptive force in the market.

Candidates must be willing to participate in at least one in-person interview.

The primary challenge in this role centers on transforming raw customer lifecycle data into intelligent, predictive engines that drive retention and optimize user experiences. Developing high-performing machine learning models is essential to uncovering hidden behavioral patterns, predicting churn, and calculating customer lifetime value. Additionally, this position tackles the integration of modern artificial intelligence, specifically through building applied AI tools like intelligent agents and chatbots to automate workflows and elevate engagement.

Success requires seamless collaboration across data engineering and analytics teams to operationalize these models and translate technical outputs into actionable business strategies.

• Develop, train, and validate machine learning models focused on key customer lifecycle events, including churn prediction and lifetime value, to directly improve departmental retention OKRs
• Extract and transform complex, large-scale data from the data warehouse to engineer high-quality features that measurably increase model accuracy and business relevance
• Leverage generative AI frameworks and large language models (LLMs) to design and deploy internal intelligent agents and conversational chatbots that automate operational tasks
• Translate sophisticated model outputs and predictive analytics into clear, actionable business strategies, partnering with analysts to design rigorous A/B tests
• Monitor the post-deployment performance of all live models, proactively identifying data drift and executing model retraining cycles to adapt to evolving customer behaviors
• Design and build interactive dashboards that visualize the tangible, data-driven outcomes of machine learning and AI initiatives for cross-functional stakeholders

• Strong proficiency in Python for complex data manipulation and statistical modeling, combined with advanced SQL capabilities for large-scale data extraction
• Deep understanding of traditional machine learning algorithms, including regression, classification, and tree-based models, along with their respective evaluation metrics
• AI Literacy and Application skills, specifically a foundational understanding of LLMs, prompt engineering, and modern generative AI frameworks to build intelligent tools
• Expertise in data visualization and data interpretation, utilizing tools like Tableau or Power BI to translate complex technical findings into intuitive dashboards
• Critical experience in building, validating, and deploying predictive models within a professional or intensive applied academic environment
• Excellent problem-solving, collaboration, and technical communication skills, with a natural curiosity to troubleshoot complex code and clearly document processes

• Familiarity with modern cloud data stack environments such as Snowflake, Databricks, Spark, AWS, or GCP
• A Master’s degree in a quantitative field with strong applied academic or internship experience in data science

• Minimum Education:
  
  Bachelor’s Degree in Data Science, Statistics, Computer Science, Mathematics, or a highly quantitative field
• Minimum Experience:
  
  1 year of professional data science experience, or a Master’s degree in a quantitative field with applied academic/internship experience
• Required Technical
  
  Skills:
  • Python…