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