Lead - Analytics

Roles and Responsibilities Direct business planning efforts across all lines of business and functional areas. Drive strategic initiatives for the organization in line with the gaps identified through strong MIS & governance. Carry out specific management projects relating to the strategic roadmap, corporate strategy & business planning. Collaborate with functional leaders and teams to implement and institutionalize change. Prepare the road map for growth and assist in developing business plans. Work on the organizational strategic leavers along with the CEO for improvement & tracking. Build effectiveness and efficiency within the organization. Playing an influencer role with various stakeholders to get the correct status of the projects and work out possible solutions. Providing all necessary support for seamless execution of projects in their scale up journey. Conduct time to time risk analysis of business opportunities.

7+ Years of Experience in Domain (Telecom/IT/OEM) with 3-5 experience in handling Large Scale Telecom Network Domain Knowledge of service performance KPIs/SLA benchmarking and strategies for continual performance improvement. Domain Experience: 10+ Years of total experience in IT/Telecom/OEM domains, with a minimum of 5–7 years specifically handling Large-Scale Data Engineering and AI/ML model production ecosystems (p. 2). Big Data Mastery: Strong hands-on expertise with PySpark for distributed processing, Apache Iceberg for transactional lakehouses, and Impala for high-performance interactive SQL querying on massive datasets. Generative AI Expertise: Deep technical understanding of transformer-based architectures, model quantization techniques (GPTQ/AWQ), fine-tuning approaches (LoRA/QLoRA), and advanced context window scaling for both LLMs and resource-constrained SLMs. Framework Fluency: Proven track record in orchestrating production-grade workflows using frameworks like LangChain, AutoGen, LlamaIndex, or CrewAI combined with custom vector databases and Graph databases .

Key Performance Indicators (Max 5)

Network Quality of Service (QoS) Adherence : Adherence to strict latency, jitter, and packet loss targets across the IP transport domain. Capacity & Bandwidth Utilization : Optimization of link utilization percentages to prevent backhaul congestion during peak traffic hours Model Inference Efficiency: Adherence to strict response latency, throughput, and token-cost targets across deployed enterprise GenAI apps. Data Processing & Pipeline Latency: Percentage reduction in data-ingestion cycle times through optimized PySpark execution and Iceberg partition layouts. Model Accuracy & Precision: Achievement of >95% accuracy/F1-scores or alignment matrix evaluation scores across customized enterprise LLM/SLM tasks. System Availability & Scalability: Adherence to AI system uptime SLAs, parallel execution scale limits, and Mean Time to Repair (MTTR) metrics for operational pipeline failures Query Execution Performance: Optimization of Impala analytics engine utilization percentages during heavy concurrent retrieval requests

Annual Budget Owned / Key Quantitative Parameters like Workforce managed etc.

Risks, Challenges, Job Context (Short Description)

Challenges:

Managing hyper-volume, multi-structured telecommunication text, voice, and network telemetry logs under strict privacy parameters. Navigating fast-evolving AI trends to systematically upscale infrastructure for newer concepts like Agentic AI workflows, hybrid RAG (Retrieval-Augmented Generation), and real-time streaming pipelines

Critical Risks

Model Hallucination & Data Leakage: Risks associated with poor prompt alignment, leaking proprietary enterprise data via LLM prompts, or providing wrong automated resolutions to end consumers. Lakehouse Partition Sluggishness: Data scale outstripping query performance limitations due to suboptimal Iceberg structure layouts or inefficient Spark cluster allocations