Senior Engagement Manager- DE & Analytics Manufacturing Expert

EXL•United States,

12d

About The Position

EXL is seeking a client-facing Manufacturing Analytics, Data & AI Transformation Leader to drive growth, consulting, solutioning, and delivery for strategic manufacturing and automotive clients. The role combines executive stakeholder management, business consulting, analytics leadership, data modernization, AI transformation, account growth, and delivery oversight. The successful candidate will advise client executives and independently lead business discussions, solutioning workshops, executive presentations, growth conversations, and client engagements valued up to $10M–$15M annually. The role leads analytics and AI transformation across manufacturing and automotive domains, including operations, supply chain, procurement, logistics, production and demand planning, inventory optimization, quality, warranty, connected manufacturing, engineering, plant performance, and industrial AI. It translates business priorities into practical operating-model, process, data, analytics, and technology interventions, with a focus on measurable business outcomes. The position also defines data and AI strategies, transformation roadmaps, and use-case portfolios; guides data-platform modernization and analytics enablement; and supports machine learning, generative AI, agentic AI, and decision-intelligence solutions. It leads solution design, proposals, deal shaping, account growth, commercial planning, and executive oversight of programs from discovery through implementation and managed delivery. The role further builds manufacturing analytics, data, AI, and GenAI capabilities, reusable assets, industry frameworks, talent, and thought leadership.

Requirements

12–18 years of experience across analytics, data, AI, digital transformation, management consulting, or technology-enabled business transformation.
At least five years of client-facing leadership experience across analytics, data, AI, or transformation engagements.
Proven ability to independently manage strategic client relationships and portfolios or engagements valued up to $10M–$15M annually.
Experience leading executive discussions, steering committees, transformation workshops, roadmap development, and solution presentations.
Ability to create executive narratives connecting data and analytical insights to business priorities and financial outcomes.
Strong understanding of the end-to-end data and AI lifecycle, including data engineering, data management, cloud data platforms, business intelligence, advanced analytics, machine learning, generative AI, deployment, governance, and adoption.
Experience leading proposals, RFPs, solution architecture, staffing, effort estimation, cost estimation, commercial inputs, and transition planning.
Ability to lead multidisciplinary teams across business consulting, data engineering, analytics, AI, architecture, program management, and change enablement.
Bachelor’s or master’s degree in engineering, computer science, data, analytics, operations, business, or a related discipline.
Executive presence and a trusted-advisor mindset.
Consultative selling and commercial orientation.
Executive storytelling, influence, and presentation capability.
Manufacturing and operational business acumen.
Strategic thinking with execution discipline.
Financial acumen and an account-growth mindset.
Client relationship management and stakeholder alignment.
Team leadership, coaching, and capability building.
Innovation mindset and ownership of measurable outcomes.

Nice To Haves

Experience in Manufacturing Analytics, Automotive, Mobility, Industrial Manufacturing, Heavy Equipment, Industrial Products, Aerospace, Transportation, or related manufacturing-intensive sectors.
Automotive OEM, Tier 1 supplier, connected vehicle, manufacturing engineering, plant operations, supply chain, quality, warranty, or aftersales experience.
Experience working with large, complex global manufacturers or industrial enterprises with distributed plants, functions, and technology environments.
Experience supporting automotive OEMs, automotive suppliers, industrial manufacturers, equipment manufacturers, or engineering-intensive enterprises.
Experience with Azure, AWS, GCP, Databricks, Snowflake, Microsoft Fabric, Power BI, Tableau, or comparable data and analytics ecosystems.
Exposure to smart manufacturing, Industry 4.0, connected factory, digital thread, digital twins, predictive maintenance, industrial IoT, and manufacturing execution environments.
Experience shaping or leading GenAI use cases in engineering, manufacturing, supply chain, quality, warranty, service, knowledge management, or commercial operations.
Experience building an industry offering, center of excellence, reusable assets, strategic partnerships, or high-performing multidisciplinary teams.

Responsibilities

Build trusted relationships with client executives and senior business, data, digital, operations, engineering, supply chain, and technology leaders.
Lead steering committees, quarterly business reviews, transformation workshops, roadmap discussions, and executive working sessions.
Translate business priorities and operational challenges into analytics, data, AI, and digital transformation initiatives.
Present findings, recommendations, and investment cases through executive storytelling and data-driven narratives.
Serve as the primary client-facing leader for strategic engagements and maintain alignment across client and EXL leadership teams.
Lead analytics and AI programs across manufacturing operations, supply chain, procurement, logistics, production planning, demand planning, and inventory optimization.
Shape solutions for quality analytics, warranty analytics, connected manufacturing, engineering analytics, plant performance, digital manufacturing, and industrial AI.
Identify use cases supporting throughput, productivity, quality, cost, service, working capital, asset performance, safety, and sustainability outcomes.
Connect business priorities with operating-model, process, data, analytics, and technology interventions.
Define enterprise analytics and AI strategies, transformation roadmaps, and prioritized use-case portfolios.
Lead data-platform modernization, cloud migration, data engineering, data management, governance, quality, integration, and analytics enablement initiatives.
Guide descriptive, predictive, prescriptive, optimization, simulation, forecasting, root-cause analysis, and operational-intelligence solutions.
Enable machine learning, generative AI, agentic AI, and decision-intelligence solutions through scalable and governed data foundations.
Partner with architects, engineers, data scientists, and business teams to ensure solutions are secure, scalable, adoptable, and tied to measurable outcomes.
Lead solution design and consulting engagements for focused initiatives and complex multi-workstream transformation programs.
Own or contribute to proposals, RFPs, RFIs, statements of work, executive pitches, and deal-shaping discussions.
Develop transformation roadmaps, delivery approaches, staffing models, workplans, effort and cost estimates, assumptions, dependencies, and risks.
Present differentiated solutions to executive stakeholders and guide opportunities from qualification through closure and transition to delivery.
Collaborate with sales, account, industry, capability, architecture, and delivery leaders to expand strategic client relationships.
Provide independent leadership for client portfolios and engagements valued up to $10M–$15M annually.
Develop account growth strategies and identify cross-sell and upsell opportunities across analytics, data, AI, GenAI, and transformation services.
Build executive relationships across client functions and create a forward-looking opportunity pipeline.
Apply financial and commercial discipline to scope, pricing, margins, staffing, utilization, and delivery commitments.
Provide executive oversight across multiple programs and workstreams from discovery through implementation and managed delivery.
Establish program governance, monitor risks and dependencies, and resolve delivery and stakeholder issues in a timely manner.
Ensure high-quality outcomes, customer satisfaction, effective resource planning, and disciplined performance management.
Mentor delivery managers, engagement leads, solution architects, analytics leaders, and technical teams.
Build and scale Manufacturing Analytics, Data, AI, and GenAI capabilities, solutions, and reusable assets.
Develop industry-specific frameworks, maturity assessments, accelerators, reference architectures, case studies, and points of view.
Contribute to hiring, team building, talent development, competency planning, and communities of practice.
Drive thought leadership and innovation across smart manufacturing, connected operations, digital thread, digital twins, industrial data, and AI-enabled decision-making.