Delivery Head - Data Science

Job Summary

We are seeking a seasoned Data Science Delivery Leader to lead the end-to-end delivery of large, complex Data Science, Advanced Analytics, AI, GenAI, and Agentic AIdriven programs across multiple business verticals. This role is part of Data Science Practice and works horizontally with vertical and functional teams to ensure consistent, high-quality delivery outcomes.

The ideal candidate will bring deep technical expertise in Data Science, Machine Learning, Generative AI, and Agentic AI systems, strong program and project management skills, and proven experience managing large delivery teams and enterprise-scale programs. This is a pure delivery leadership role with no P&L, capability building, or practice management responsibilities.

Key Responsibilities

1. Delivery & Technical Oversight

Own and drive end-to-end delivery of large-scale, multi-year Data Science, AI, GenAI, and Agentic AI programs, ensuring adherence to scope, timelines, quality, and cost.

Provide strong technical oversight across solution design, ML/AI model development, GenAI solution architecture (LLMs, RAG, fine-tuning), agent workflows, validation, deployment, and productionization.

Ensure delivery aligns with agreed enterprise architectures, data standards, security, Responsible AI, and governance requirements.

Act as the senior escalation point for critical technical, architectural, and delivery issues, including GenAI and AI-agent related risks.

2. Program & Project Management

Lead multiple concurrent programs and projects using Agile, hybrid, or waterfall delivery models, including complex AI and GenAI engagements.

Establish and run delivery governance, cadence reviews, and executive-level status reporting for AI and GenAI initiatives.

Identify, assess, and mitigate delivery risks and dependencies proactively, including model performance, scalability, ethical AI, and regulatory risks.

Ensure effective coordination across cross-functional teams, platform teams, vendors, and stakeholders.

3. Team Leadership (Delivery-Focused)

Lead and manage delivery teams of data scientists, ML engineers, GenAI engineers, data engineers, and analysts to ensure execution excellence.

Set clear delivery goals, roles, and accountability for large, distributed program teams.

Drive performance management, resource planning, and delivery productivity across traditional AI and GenAI/Agentic AI workloads.

4. Stakeholder Management

Partner closely with business and vertical leaders to translate business requirements into scalable AI, GenAI, and agent-based delivery plans.

Communicate program progress, risks, and outcomes clearly to senior leadership and client stakeholders, including AI-specific metrics and value realization.

Ensure high levels of stakeholder satisfaction through predictable, transparent, and outcome-driven delivery.

5. Delivery Excellence

Ensure consistent application of AI/ML and GenAI delivery best practices, tools, and quality benchmarks.

Drive continuous improvement in delivery processes to enhance efficiency, reliability, and reusability of AI and GenAI solutions.

Support presales and transition activities through delivery planning, solution estimates, execution readiness, and risk assessments (as required).

Required Qualifications

17+ years of overall experience, with 810+ years in Data Science / Advanced Analytics / AI delivery roles.

Strong hands-on technical foundation in Machine Learning, AI, Generative AI (LLMs, RAG, fine-tuning), and Agentic AI concepts, with the ability to guide teams in depth.

Proven experience managing large programs, multi-project portfolios, and complex AI/GenAI delivery engagements.

Solid expertise in program and project management methodologies and delivery governance models.

Experience with tools and technologies such as Python, R, ML frameworks (TensorFlow, PyTorch, scikit-learn) and exposure to LLM ecosystems.

Excellent communication, stakeholder management, and problem-solving skills.

Preferred Skills

Experience delivering AI, GenAI, and agent-based solutions on cloud platforms (AWS, Azure, GCP).

Exposure to LLM platforms, vector databases, orchestration frameworks, and AI pipelines.

Exposure to big data ecosystems (Spark, Hadoop, Hive).