Data Scientist

Job Overview:The Data Scientist plays a pivotal role in extracting valuable insights from complex datasets and developing innovative machine learning models to solve business challenges. This role ensures that data-driven insights are accurately translated into actionable recommendations, enabling stakeholders across the organization to make informed strategic decisions. The ideal candidate has deep expertise in statistical analysis, machine learning algorithms, and data visualization techniques.

This role involves working closely with cross-functional teams, including Senior Data Engineers, Product Managers, and Business Analysts, to deliver impactful data solutions. A successful Data Scientist will not only build and deploy effective predictive models but also contribute to the evolution of the company's analytics strategy, ensuring alignment with industry best practices and emerging technologies. Responsibilities:Contact Center Analytics & LLM IntegrationDevelop custom LLM-powered virtual agents with domain-specific tuning and retrieval augmentation (RAG) to enhance self-service capabilities in contact centers.

Implement conversational intelligence frameworks using fine-tuned foundation models to analyze customer-agent interactions for sentiment analysis, intent classification, and automated summarization. Design LLM evaluation frameworks and guardrail systems to ensure appropriate agent responses while maintaining compliance standards. Cross-Functional Team CollaborationPartner with Senior Data Engineers to design scalable data architectures that support both model training and inference requirements for AI applications.

Collaborate with Machine Learning Engineers to transform prototype models into production-ready systems with appropriate monitoring and maintenance protocols. Work closely with DevOps Engineers to implement CI/CD pipelines for model deployment, versioning, and automated testing of AI applications. Coordinate with Automation Engineers to integrate AI models into broader process automation workflows and ensure seamless data flow between systems.

Support Web Developers by providing optimized model APIs and integration patterns for embedding AI capabilities into web applications and customer portals. Align with AI Developers on consistent approaches to foundation model usage, prompt engineering standards, and shared knowledge repositories. Collaborate with UAT Leads to define appropriate test scenarios, success metrics, and evaluation methodologies for AI system validation.

AI Agent Development & OrchestrationBuild multi-agent systems that coordinate specialized AI agents for handling complex customer queries through reasoning, planning, and tool usage capabilities. Develop agent-based simulation environments to test and optimize contact routing strategies and virtual agent performance under various load conditions. Implement agent supervision mechanisms that enable seamless escalation from AI to human agents with full conversation context preservation.

LLM-Powered Customer ExperienceCreate personalized recommendation engines using hybrid LLM-based approaches combined with collaborative filtering for next-best-action suggestions. Develop custom instruction-tuned models for customer sentiment analysis and churn prediction with explainable outputs for actionable insights. Implement LLM-based content generation systems for personalized customer communications across channels with brand voice consistency.

AI Workflow AutomationDesign end-to-end AI workflow orchestration frameworks that connect data ingestion, preprocessing, LLM inference, and action execution across enterprise systems. Develop automated LLM prompt engineering workflows with systematic testing and optimization to maximize model performance for domain-specific tasks. Implement AI-driven workflow analytics to identify automation opportunities and measure impact of implemented AI solutions.

Database Architecture & OptimizationDesign and optimize SQL database schemas (PostgreSQL, MySQL, SQL Server) to support AI applications with efficient query performance for high-volume transaction processing. Develop advanced query optimization strategies using indexing, partitioning, and query plan analysis to enable real-time analytics on operational databases. Implement NoSQL database solutions (MongoDB, Cassandra, Redis) for specific use cases including session management, caching, and handling semi-structured data at scale.

Vector Database ImplementationDesign and deploy vector database architectures (Milvus, Pinecone, Weaviate, Qdrant) optimized for semantic search and retrieval-augmented generation applications. Implement hybrid search strategies combining vector similarity with metadata filtering for context-aware knowledge retrieval. Develop efficient embedding pipelines and chunking strategies for document ingestion with optimal retrieval performance.

Knowledge Sharing & DocumentationCreate comprehensive documentation for AI models, data pipelines, and experimental results to enable knowledge transfer across development teams. Develop internal educational resources on AI technologies and methodologies to upskill team members across technical disciplines. Present findings and insights from data analysis to both technical and non-technical stakeholders to drive data-informed decision making.

Participate in code reviews, architectural discussions, and technical planning sessions to ensure alignment with organizational standards and best practices. Data Integration & EngineeringArchitect ETL/ELT pipelines connecting heterogeneous database systems (SQL, NoSQL, data warehouses) with proper transformation logic and data quality validations. Implement real-time data synchronization between operational databases and analytical systems using change data capture and event streaming.

Design data models that effectively support both transactional processing and analytical workloads with appropriate normalization strategies. Technical Support Enhancement with AIBuild intelligent support agents capable of troubleshooting technical issues through multi-turn conversations and tool usage. Develop LLM-powered knowledge retrieval systems with semantic search to surface relevant documentation and solution approaches.

Implement predictive support models that combine telemetry data with LLM reasoning to anticipate and prevent technical issues.