I&P - GN - SONG - AI & Data - Service - Decision Science - Manager

Job Title – Decision Science Manager – S&C GN Management Level: 07 – Manager Location: Bengaluru | Gurugram | Mumbai | Kolkata | Pune | Chennai | Hyderabad Must Have Skills: Agentic AI, Generative AI, LLMs, Contact Centre AI, GCP/Azure/AWS, Data Science, Consulting, RFP/RFI, SOW, Program Delivery Good to Have Skills: UI/UX, CI/CD, DevOps, Enterprise AI Solution Delivery

Job Summary As a Decision Science Manager within the S&C GN Customer Service Analytics & Contact Center AI practice, you will manage enterprise AI transformation programs across strategy, solution design, RFP/RFI responses, SOW development, delivery execution and program management. You will drive business development, client engagement while delivering Agentic AI, Generative AI, Conversational AI and LLM-based solutions on GCP, Azure and AWS. Roles & Responsibilities · Manage end-to-end Contact Centre AI workstreams from discovery and use-case definition through solution delivery, adoption, value tracking, and transition to operations. · Translate business challenges into prioritized AI use cases, requirements, roadmaps, business cases, and measurable improvements in containment, handle time, resolution, quality, customer satisfaction, and cost to serve. · Design and deliver scalable Agentic AI, Generative AI, LLM, Conversational AI, speech/text analytics, agent assist, automation, and knowledge solutions across cloud, data, integration, security, observability, and responsible AI. · Develop consulting and commercial deliverables, including assessments, operating-model recommendations, RFP/RFI responses, proposals, SOW inputs, estimates, staffing plans, timelines, risks, dependencies, and acceptance criteria. · Manage program governance across scope, schedule, budget, quality, resources, RAID, change, benefits, and status reporting; facilitate workshops and align business, technology, data, security, and operations stakeholders. · Guide cloud AI implementation across GCP, Azure, or AWS and work with architecture and engineering teams on integration, testing, release readiness, performance, security, monitoring, and production support. · Lead and coach multidisciplinary teams of 4+ consultants, data scientists, engineers, and delivery professionals; plan work, review deliverables, resolve issues, and maintain quality and client satisfaction. · Support account and practice growth by identifying opportunities, shaping demonstrations, contributing to pursuits, and developing reusable assets, accelerators, case studies, and thought leadership. Professional & Technical Skills Must Have Skills · 10+ years of overall experience, including 6+ years in Data & AI, analytics, consulting, or contact centre transformation, with responsibility for enterprise workstreams, deliverables, teams, and client outcomes. · Proven delivery experience across Agentic AI, Generative AI, LLMs, Conversational AI, machine learning, speech/text analytics, agent assist, automation, and Contact Centre AI capabilities. · Strong understanding of LLM solution patterns, including prompting, agent and tool orchestration, RAG, vector search, grounding, guardrails, human review, evaluation, monitoring, and responsible AI controls. · Hands-on expertise in at least one of GCP, Azure, or AWS, with working knowledge of enterprise data platforms, APIs and integration, identity and security, privacy, scalability, resilience, and observability. · Strong consulting skills across discovery, requirements, use-case prioritization, business cases, roadmaps, solution design, executive presentations, RFP/RFI responses, proposal development, estimates, and SOW inputs. · Demonstrated delivery, team, and stakeholder management covering Agile or hybrid methods, planning, financial tracking, resourcing, governance, risk, quality, change, client communication, coaching, and issue resolution. Good to Have Skills · Experience with CCaaS and customer-service platforms or technologies such as Genesys, Amazon Connect, NICE, Five9, Salesforce, Microsoft Dynamics, IVR, routing, workforce management, and quality management. · Knowledge of MLOps/LLMOps, CI/CD, DevOps, containerization, Kubernetes, automated testing, model and prompt lifecycle management, and leading cloud AI, agent, and data platforms. · Exposure to conversational UX, AI-enabled knowledge management, relevant cloud or AI certifications, and development of reusable assets, accelerators, demonstrations, or go-to-market offerings.