Senior Data Scientist

Job Duties And Responsibilities

Candidates must be willing to participate in at least one in-person interview, which may include a live whiteboarding or technical assessment session. Multi-brand telecom and technology environments face operational complexity and resource inefficiencies when deploying large-scale artificial intelligence models across hybrid infrastructures. This role addresses these challenges by driving the strategic evaluation and deployment of advanced AI architectures through statistical rigor and predictive modeling. The position designs autonomous agentic AI pipelines and governance frameworks to guarantee model reliability across cloud-native and on-premises environments. Delivering high-fidelity simulations and benchmarks directly resolves subscriber churn and optimizes network operational efficiency.

What Success Looks Like (Objectives)

• Optimize model performance and cost-efficiency by benchmarking execution pathways between cloud-native and on-premises environments for large-scale deployments.
• Lead the design and statistical validation of multi-agent architectures using LangGraph and AWS Bedrock to automate network provisioning and achieve autonomous operational workflows.
• Enhance network efficiency by developing high-accuracy predictive models for churn prevention, anomaly detection, and time-series forecasting.
• Maintain enterprise model integrity and long-term validity through automated experiment tracking, versioning, and drift detection using Databricks Unity Catalog.
• Translate complex quantitative outputs into actionable executive scorecards and Digital Twin policy simulations to guide business strategy, vendor evaluations, and alignment with NIST AI RMF standards.

Skills, Experience And Requirements

• Core Skills and Competencies (What You’ll Bring)
• Advanced proficiency in AI innovation, specifically the application of agentic frameworks such as LangChain or LangGraph to solve autonomous operation challenges.
• Strong expertise in machine learning development using Python, PyTorch, and TensorFlow to construct models with high predictive power and statistical reliability.
• Critical experience in LLM deployment strategies, including RAG optimization, fine-tuning, and performance inference across hybrid cloud environments.
• Deep technical understanding of MLOps lifecycle management within modern data platforms like Databricks or SageMaker to ensure scalable model delivery.
• Collaborative professional expertise in data storytelling, translating complex quantitative findings into strategic recommendations for C-suite stakeholders.
• Thorough knowledge of AI governance and responsible AI principles, including NIST AI RMF and TOGAF standards, to mitigate model risk and ensure ethical data application.

Additional Qualifications

• Familiarity with TM Forum ODA or telecom-specific data models.
• Exposure to on-premises AI infrastructure, including GPU cluster provisioning or Kubernetes platforms.
• Background in knowledge graphs or enterprise search optimization.

Minimum Requirements

• Minimum Education: Bachelor’s Degree in Computer Science, Data Science, Statistics, Electrical Engineering, or a related quantitative field.
• Minimum Experience: 5+ years of experience in data science.
• Required Technical Skills: Must have at least 2 years of experience with:
• - Python (Scikit-learn, PyTorch, or TensorFlow)
• - Databricks, Spark, or AWS (SageMaker/Bedrock)
• - SQL and cloud-native orchestration (dbt or Airflow)

Salary Ranges

Compensation: $96,250.00/Year - $137,500.00/Year

Benefits

We offer versatile health perks, including flexible spending accounts, HSA, a 401(k) Plan with company match, ESPP, career opportunities, and a flexible time away plan; all benefits can be viewed here: EchoStar Benefits. The base pay range shown is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, and competencies; compensation is based on the role's location and is subject to change based on work location.