Decision Scientist

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.

We are seeking a technically advanced Decision Scientist to drive intelligence across our sales and retention channels (Boost Mobile stores, eCommerce, Amazon, Telesales, and B2B). This is role fusion of data analytics, data science, and strategy. In this role, you will move beyond reporting what happened to explaining why it happened and predicting what will happen next. You will serve as a technical bridge, utilizing statistical methods, predictive churn modeling, and Generative AI to enhance our customer lifecycle strategy.

Your leadership will be crucial in transforming raw data into a roadmap for customer retention and revenue growth.

• Executive Communication & Data Storytelling
  • Strategic Narrative:
    Act as the primary translator of complex statistical findings for the C-Suite and non-technical leadership
  • Impact Visualization:
    Move beyond basic charts to create visual data stories that highlight the "So What?" and "Now What?" of the analysis; specifically, guide leadership on budget allocation for acquisition vs. retention based on data evidence
  • ROI Demonstration:
    Regularly present findings in high-stakes meetings, clearly demonstrating how data science models are directly impacting revenue, churn reduction, and Net Promoter Scores (NPS/CSAT)
• Predictive Churn Modeling & Retention Strategy
  • Proactive Retention:
    Evolve retention strategies from reactive reporting to proactive prediction. Build and maintain predictive churn models (using Python/R/XGBoost) to identify at-risk customers before they leave
  • Survival Analysis:
    Analyze survival curves and hazard rates to pinpoint specific lifecycle moments (e.g., Day 90, Month 12) where customers are most vulnerable to churn
  • Intervention Testing:
    Partner with marketing and retention teams to design and test targeted interventions based on individual churn probability scores
• AI Innovation & LLM Prototyping
  • Unstructured Data Mining:
    Leverage Large Language Models (LLMs) and NLP techniques to analyze unstructured text data (e.g., chat logs, survey verbatims, telesales transcripts) to identify sentiment shifts and emerging churn drivers
  • PoC Development:
    Act as a "prototyper" for the engineering team. You will build initial Proof-of-Concept (PoC) models locally to solve immediate business problems, then collaborate with engineers to scale successful models into production
• Advanced Lifecycle & Segmentation Analysis
  • Dynamic Personas:
    Perform unsupervised learning (clustering/segmentation) on demographic, behavioral, and transactional data to create living customer personas
  • CLV Optimization:
    Conduct Customer Lifetime Value (CLV) analysis to help the business prioritize high-value segments for white-glove service or exclusive offers
  • Journey Mapping:
    Map the end-to-end customer journey to identify friction points that lead to early drop-off or failed activations
• Data Infrastructure & Visualization
  • Cloud-Native Analytics:
    Write complex SQL queries to wrangle and join distinct datasets from sales, marketing, and web tracking sources within a cloud environment
  • Automated Intelligence:
    Build automated, interactive dashboards (Tableau) that incorporate statistical baselines, trend lines, and automated forecast alerts
• Continuous Improvement
  • Stay abreast of industry trends, best practices, and emerging technologies in sales analytics and AI. Proactively seek out opportunities to upgrade our sales channel analysis methodologies

• Education and Experience:
  • Bachelor’s or Master’s degree in Statistics, Computer Science, Data Science, or a quantitative business field
  • 3–5+ years in an advanced analytics role with a focus on customer behavior
  • Cloud Computing (AWS):
    Experience working within the AWS ecosystem is required. Specifically S3 (storage), Athena (SQL querying), and familiarity with Sage Maker or Lambda for automating analysis scripts
  • Generative AI & NLP:
    Practical experience applying LLM APIs (e.g., OpenAI, Gemini, Hugging Face, or Llama) to business problems, such as text summarization, sentiment analysis, or entity extraction
  • Coding:
    Proficiency in Python or R is required for statistical modeling (pandas, scikit-learn) and API integration
  • Visualization:
    Strong experience with Tableau, Power
    
    BI, or Looker
• Skills and
  
  Qualifications:
  • Statistical Depth:
    Practical application of regression…