Senior Data Scientist, AI Analytics

The Senior Data Scientist, AI Analytics develops predictive modeling and generative AI frameworks to solve operational inefficiencies across business domains. This role executes the translation of complex datasets into resilient production machine learning pipelines and algorithmic solutions that enhance customer lifetime value and retention. Key challenges center on standardizing model reproducibility, monitoring algorithmic drift across production environments, and supporting automated compliance workflows. Success requires applying statistical theory to inform business decisions, delivering operational efficiency, and driving continuous technological innovation. What Success Looks Like (Objectives): Partner with operational and engineering leaders to establish machine learning roadmaps that align with enterprise OKRs and product objectives Design, deploy, and maintain scalable production machine learning systems for customer segmentation, predictive churn mitigation, and lifetime value optimization Implement and scale generative AI initiatives by engineering robust LLM-assisted workflows for automated multi-channel transcript analysis and real-time compliance auditing Establish automated data science pipelines across enterprise cloud infrastructures to support model reproducibility, auditability, and continuous delivery Leverage AI developer tooling and automated monitoring frameworks to optimize model training efficiency, track pipeline latency, and streamline deployment cycles Translate complex model outputs into analytical dashboards and actionable recommendations that inform operational decisions across core business units Core Skills, Competencies, and Key Experiences (What you’ll bring) Proven capability in designing, deploying, and maintaining production-grade predictive machine learning models, including classification, regression, and time-series forecasting Applied AI proficiency in prompt engineering, model validation, and deployment of large language models within production environments Strong technical capabilities in Python, Scala, and SQL, coupled with hands-on proficiency in machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch Ability to design and optimize distributed data science pipelines within enterprise cloud platforms such as AWS, Databricks, or Snowflake Deep expertise in experimental design, causal inference, and rigorous A/B testing methodologies to validate algorithmic impact against critical business metrics Collaborative problem-solving and communication skills to convey statistical findings and technical roadmaps to cross-functional stakeholders and leadership   Candidates must be willing to participate in at least one in-person interview.

Minimum Education: Bachelor’s Degree in Statistics, Machine Learning, Computer Science, Engineering, Mathematics, Physics, or a related quantitative field Minimum Experience: 4 years of experience in data science and applied machine learning Required Technical Skills: Must have at least 4 years of experience with: Python, Scala, and SQL Machine learning libraries such as Scikit-learn, TensorFlow, or PyTorch Cloud-based AI and data platforms such as AWS, Databricks, or Snowflake Unstructured data processing for natural language processing (NLP) or generative AI workflows   Visa sponsorship not available for this role #LI-JZ2  Salary Range Compensation: $96,250.00/Year - $137,500.00/Year About Our Department Our Technology teams challenge the status quo and reimagine capabilities across industries. Whether through research and development, technology innovation or solution engineering, our team members play a vital role in connecting consumers with the products and platforms of tomorrow. About Our Company EchoStar 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