The ideal candidate has deep delivery leadership experience with data/analytics/AI programs and is comfortable engaging senior stakeholders while running multidisciplinary teams.

Key Responsibilities :

1. Portfolio & Delivery Ownership :

- Take end-to-end responsibility for a portfolio of data, BI, AI/ML, and GenAI programs, ensuring on-time, on-budget, high-quality delivery.

- Set up and run delivery governance steering meetings, status reviews, risk and issue logs, and escalations to ensure no surprises program health.

- Ensure consistent adoption of delivery processes (agile/hybrid), documentation standards, and QA practices across all engagements.

- Proactively identify and manage delivery risks, dependencies, and scope changes; drive clear mitigation and communication.

2. Client Experience & Stakeholder Management :

- Serve as the primary delivery point of contact for senior client stakeholders (CXOs, business heads, product/tech leaders).

- Translate client objectives into clear roadmaps, milestones, SLAs, and outcomes; track and report against them regularly.

- Own client experience metrics (e.g., NPS / CSAT for your portfolio), driving corrective actions where needed.

- Navigate complex, matrixed client environments and manage stakeholder expectations across business, technology, and vendor teams.

3. Commercial Management & Account Health :

- Own delivery-side financials staffing, effort and cost tracking, utilization, margin health, change requests, and renewals.

- Work with account owners to build and maintain account plans linking delivery roadmap, risks, and opportunities to revenue and margin goals.

- Ensure billing hygiene, forecasting accuracy, and efficient resource allocation across programs and locations (onsite/offshore/hybrid).

4. Demand Generation & Growth Support :

- Partner with sales and solution leaders on new opportunities, proposals, and SoWs shaping delivery models, phasing, and effort estimates.

- Leverage delivery insight to identify expansion opportunities : new use cases, new stakeholders, and scaling of successful pilots.

- Contribute to thought leadership, case studies, and reference stories emerging from delivered programs.

5. People Leadership & Capability Building :

- Lead and mentor multi-disciplinary teams (project/program managers, BAs, data engineers, data scientists, AI/ML/GenAI engineers, BI developers, QA, etc.).

- Build a strong second line of delivery leadership, including grooming Delivery Managers / Program Leads for higher responsibility.

- Work with practice and HR teams on hiring, workforce planning, and systematic upskilling aligned to portfolio needs (cloud data, MLOps, GenAI, etc.).

- Foster a culture of ownership, collaboration, and continuous improvement across teams.

6. Quality, Process & Continuous Improvement :

- Institutionalize delivery standards, playbooks, and checklists for your portfolio (estimation, design reviews, testing, documentation, security, and data privacy).

- Drive regular retrospectives and bring portfolio-level improvements (tools, automation, templates, reusable components).

- Ensure compliance with internal and client policies, including security, IP protection, and responsible use of AI where applicable.

What You Bring :

Experience & Background (Must-Have) :

- 1218 years of experience in consulting / analytics / data & AI, with at least 5+ years in delivery leadership across multiple concurrent programs.

- Able to define end-to-end data & AI solutions and articulate a clear value narrative, turning client problem statements into structured roadmaps, architectures, and execution plans.

- Comfortable presenting POVs and solution approaches to CXO-level stakeholders, leading strategic conversations, handling objections, and aligning diverse stakeholders around a common direction.

- Proven track record of owning delivery and account health for global clients including scope, quality, timelines, utilization, and margins.

- Strong familiarity with data platforms and analytics/AI solution delivery data engineering, BI, AI/ML, and emerging GenAI patterns (conceptual and architectural level).

- Experience defining and running delivery processes (waterfall and agile), setting up offshore teams, and implementing governance and quality frameworks.

- Demonstrated success working with senior stakeholders in complex, matrixed environments, handling escalations and aligning diverse teams around shared goals.

Skills & Competencies :

- Strong program and portfolio management abilities planning, dependency management, risk control, and executive-level communication.

- Comfort with cloud-based architectures, DevOps/MLOps concepts, and modern data/AI engineering practices (hands-on not required, but you can challenge and guide teams).

- Ability to connect business value to delivery quantify impact, measure adoption, and frame delivery status in business terms.

- Structured thinking and problem-solving skills; ability to bring order and clarity to ambiguous asks.

Education (Preferred) :

- Bachelors degree in Engineering / Computer Science or related field;

- MBA or equivalent business/management qualification is preferred but not mandatory.