EchoStar is looking to leverage data science and GenAI to drive business outcomes by developing machine learning models for customer segmentation, churn prediction, user modeling, and lifetime value estimation, and by designing and deploying GenAI and LLM solutions to automate complex workflows.
Requirements
- Strong foundation in statistical and machine learning techniques, including regression, classification, clustering, neural networks, and ensemble models.
- Proficient in Python and SQL for data science workflows; skilled in building and evaluating generative AI and LLM-based solutions.
- Experienced in prompt engineering, model evaluation, and deploying LLMs in production environments.
- Skilled in processing unstructured data (text, images) using modern ML/DL frameworks.
- Proven ability to design, run, and analyze A/B tests and controlled experiments.
- Hands-on experience with AWS cloud environments and enterprise-scale ML platforms like Dataiku and Databricks.
- Experience with big data tools such as Amazon Athena, Redshift, BigQuery, Spark, or Teradata
Responsibilities
- Develop machine learning models for customer segmentation, churn prediction, user modeling, and lifetime value estimation.
- Design and deploy GenAI and LLM solutions to automate complex workflows, such as transcript analysis and compliance validation.
- Transform and analyze large-scale structured and unstructured data (e.g., text, images) to uncover actionable insights.
- Collaborate with cross-functional teams to build and deploy scalable, cloud-based data science and GenAI solutions; provide technical mentorship.
- Communicate complex models and findings to both technical and non-technical audiences via dashboards, notebooks, and presentations.
- Skilled in building and evaluating generative AI and LLM-based solutions.
- Skilled in processing unstructured data (text, images) using modern ML/DL frameworks.
Other
- Candidates must be willing to participate in at least one in-person interview, which may include a live whiteboarding or technical assessment session.
- Partner with stakeholders to identify high-impact, feasible opportunities and demonstrate how data science can drive business outcomes.
- Bachelor’s or Master’s degree in Statistics, Machine Learning, Computer Science, Engineering, Mathematics, Physics, or a related quantitative field
- 4+ years of experience in data science and applied machine learning, with a proven track record of delivering business impact from data-driven solutions
- Candidates need to successfully complete a pre-employment screen, which may include a drug test and DMV check.
