Senior Data Scientist, AI Analytics

EchoStar•Denver, CO

About The Position

The Senior Data Scientist, AI Analytics develops predictive modeling and generative AI frameworks to address operational inefficiencies across business domains. The role translates complex datasets into resilient production machine learning pipelines and algorithmic solutions intended to improve customer lifetime value and retention. Key challenges include standardizing model reproducibility, monitoring algorithmic drift in production, and supporting automated compliance workflows. The position applies statistical theory to inform business decisions, improve operational efficiency, and drive technological innovation. The role partners with operational and engineering leaders to establish machine learning roadmaps aligned with enterprise OKRs and product objectives. It designs, deploys, and maintains scalable machine learning systems for customer segmentation, predictive churn mitigation, and lifetime value optimization; develops LLM-assisted workflows for transcript analysis and real-time compliance auditing; and builds automated data science pipelines supporting reproducibility, auditability, and continuous delivery. It also uses AI developer tools and monitoring frameworks to improve training efficiency, track pipeline latency, and streamline deployments, while translating model outputs into dashboards and actionable recommendations. The position requires a bachelor's degree in a quantitative field and at least four years of data science and applied machine learning experience, including at least four years with Python, Scala, SQL, machine learning libraries, cloud-based AI and data platforms, and unstructured data processing for NLP or generative AI workflows. Candidates must be willing to participate in at least one in-person interview. Visa sponsorship is not available.

Requirements

Bachelor's degree in Statistics, Machine Learning, Computer Science, Engineering, Mathematics, Physics, or a related quantitative field.
At least 4 years of experience in data science and applied machine learning.
At least 4 years of experience with Python, Scala, and SQL.
At least 4 years of experience with machine learning libraries such as Scikit-learn, TensorFlow, or PyTorch.
At least 4 years of experience with cloud-based AI and data platforms such as AWS, Databricks, or Snowflake.
At least 4 years of experience with unstructured data processing for natural language processing (NLP) or generative AI workflows.
Proven capability 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 production deployment of large language models.
Ability to design and optimize distributed data science pipelines within enterprise cloud platforms.
Expertise in experimental design, causal inference, and A/B testing to validate algorithmic impact against business metrics.
Collaborative problem-solving and communication skills to convey statistical findings and technical roadmaps to cross-functional stakeholders and leadership.
Willingness to participate in at least one in-person interview.

Responsibilities

Partner with operational and engineering leaders to establish machine learning roadmaps aligned 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.
Engineer 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.
Use 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 for operational decisions across business units.