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.

As a Data Scientist in AI Analytics, you will design sophisticated analytical frameworks and cutting-edge generative solutions that optimize the customer journey and streamline enterprise operations. By bridging the gap between complex data capabilities and tangible business value, you identify transformative opportunities for innovation across the organization. This role requires working at the intersection of engineering and strategy to translate high-dimensional insights into actionable roadmaps while providing technical mentorship to peers.

What Success Looks Like (Objectives)

• Partner with stakeholders to identify and execute high-impact AI opportunities that directly drive measurable business outcomes
• Deliver production-grade machine learning models for customer segmentation, churn prediction, and lifetime value estimation to enhance user engagement
• Design and deploy scalable GenAI and LLM solutions that automate complex enterprise workflows, such as transcript analysis and compliance validation
• Build cloud-based data science pipelines on enterprise platforms to ensure the reliability and scalability of analytical products
• Translate complex findings into clear narratives and interactive dashboards that guide executive decision-making
• Foster technical growth across the team by mentoring peers in modern MLOps practices and data storytelling techniques

Skills, Experience and Requirements

Core Skills and Competencies (What you'll bring)

• Critical experiences in applied machine learning, including a track record of delivering high-impact classification, regression, and time-series forecasting solutions
• Advanced AI Application skills, specifically regarding prompt engineering, model evaluation, and the deployment of Large Language Models within production environments
• Technical expertise in Python, Scala, and SQL alongside a deep understanding of modern ML libraries such as TensorFlow, PyTorch, or Scikit-learn
• Hands-on experience with cloud-native data platforms like AWS, Databricks, or Snowflake to manage large-scale data engineering and processing
• Strong collaboration and data storytelling skills that allow for the communication of high-dimensional insights to diverse, non-technical audiences
• Analytical mastery in designing and interpreting A/B tests and controlled experiments to validate model performance and business hypotheses
• Familiarity with MLOps practices for monitoring and maintaining GenAI systems in production

Minimum Requirements

• Minimum Education: Bachelor's Degree in Statistics, Machine Learning, Computer Science, Engineering, Mathematics, Physics, or a related quantitative field
• Minimum Experience: 3+ years of experience in data science and applied machine learning
• Required Technical Skills:
  
  • Python, Scala, and SQL.
  • Machine learning libraries (Scikit-learn, TensorFlow, or PyTorch)
  • Cloud-based AI platforms (AWS, Databricks, or Snowflake)
  • Unstructured data processing for GenAI applications

Visa sponsorship not available for this role

Salary Ranges

Compensation: $100,980.00/Year - $136,625.00/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.

The posting will be active for a minimum of 3 days. The active posting will continue to extend by 3 days until the position is filled.