Associate Director - Analytics Consulting(Telecom/Media/Entertainment)
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Overview
Job Description
Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple cities across the US, UK, India, and Singapore, and a substantial remote global workforce.
If you are passionate about working on business problems that can be solved using structured and unstructured data on a large scale, Tiger Analytics would like to talk to you.
Responsibilities
Program Leadership & Delivery: Lead the end-to-end delivery of complex data and analytics programs. Own the roadmap, from problem definition and solution design to production-grade deployment.
Stakeholder Management: Act as the primary point of contact for client VPs and Directors. Translate unstructured business problems into high-level analytics solution designs.
Global Team Management: Lead and mentor a hybrid team of onshore (US) and offshore (Global Delivery Center) data scientists and data engineers. Ensure seamless collaboration across time zones.
Solution Architecture: Work with technical leads to design of scalable AI/ML pipelines and data architectures. Ensure solutions adhere to enterprise standards and leverage modern technology stack
Domain Expertise: Apply deep Insurance domain knowledge to validate model outputs and ensure they align with industry nuances
Commercial Impact: Present analytical findings and quantify the business value and drive the operationalization of AI into business processes.
Strategic AI Solutions Architecting: Lead the translation of complex business friction into production-ready GenAI systems, moving beyond simple chatbots to agentic workflows and multi-step reasoning architectures (using frameworks like LangChain or LlamaIndex) that deliver measurable P&L impact.
Operational Excellence & LLMOps Governance: Drive the enterprise-wide transition from "AI pilots" to scalable production by overseeing the full model lifecycle-standardizing prompt versioning, fine-tuning strategies (LoRA/QLoRA), and automated evaluation frameworks (e.g., Ragas) to ensure technical reliability and data security.
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