Company Summary
DISH Network Technologies, 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 Infinite, Boost Mobile, DISH Wireless, OnTech and GenMobile.
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.
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Job Duties and Responsibilities
- Develop and implement ML models for predictive modeling, classical ML, NLP, recommendation systems, and generative AI (GenAI) applications.
- Design and execute experiments to evaluate model performance, interpret results, and iterate on model improvements.
- Conduct data analysis to extract actionable insights and communicate findings to business stakeholders.
- Lead cross-functional projects from research and prototyping to production deployment.
- Present results and insights clearly to both technical and non-technical audiences.
Must have
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 6+ years of experience in machine learning, data science, or related roles.
- Proficiency in Python and experience with ML libraries/frameworks such as PyTorch, TensorFlow, Scikit-learn, and GenAI/Language models.
- Proven track record of building and deploying ML models
- Experience working with AWS services such as S3, Bedrock, EKS, Lambda, SageMaker AI
Good to have
- Experience with MLOps practices, including model maintenance, versioning, monitoring, and automated retraining pipelines.
- Ability to mentor junior data scientists and collaborate effectively with cross-functional teams, including data engineering, product, and business stakeholders.
- Employee Stock Purchase
- Term Insurance
- Accident Insurance
- Health Insurance
- Training Reimbursement
- Gratuity
- Mobile and Internet Reimbursement
- Team Outings