Data Scientist (Agents)

Greetings 

Data Scientist (Agents)
Irving, TX 75039

Need ,GCEAD,,EAD

Position Summary

We are seeking a Senior Data Scientist to drive innovation in customer care by designing, developing, and deploying AI-powered analytics solutions. In this role, you will utilize advanced analytics, decision science, and generative AI to create intelligent products that significantly enhance customer experiences and operational efficiency. You will translate complex data into actionable insights, ensuring AI solutions are built, adopted, and impactful. The team collaborates closely with customer service and customer experience stakeholders to address interdisciplinary business challenges at scale. The goal is to attract, retain, and enable high-performing customer service representatives by streamlining the customer journey, reducing unnecessary call volume, and improving call-center efficiency through data-driven decision-making and automation.

Key Responsibilities

• Design, develop, and deploy AI-powered analytics solutions aimed at improving customer care operations.
• Apply advanced analytics, decision science, and generative AI techniques to build intelligent, impactful products.
• Translate complex data sets into actionable insights to inform business decisions and operational improvements.
• Collaborate with stakeholders in call center operations and customer experience to solve interdisciplinary problems at scale.
• Contribute to initiatives that attract, retain, and enable top customer service talent by simplifying processes and reducing avoidable calls.
• Support automation efforts and data-driven decision-making to enhance call-center efficiency and customer satisfaction.

Must-have requirements

1. NLP (Natural Language Processing) — Most important requirement. Experience working with text data and extracting insights from call/chat transcripts.
2. Hands-on AI/ML model experience — Has built, trained, tested, or evaluated models, not just prompted existing LLMs.
3. LLM experience — Practical experience working with language models and understanding their limitations beyond basic prompting.
4. Model evaluation and validation — Experience validating model outputs against ground-truth datasets, measuring accuracy, and improving performance.
5. Strong technical foundation — Understands model behavior, nuances, and troubleshooting.
6. Stakeholder communication — Comfortable collaborating with business teams, gathering feedback, and improving model output