Delivery Lead- Senior Manager in England

With us, you'll learn fast, work smart, and make a difference. You'll build a career that matters.

Ready to build the future with AI? At Genpact, we don't just keep up with technology—we set the pace. AI and digital innovation are redefining industries, and we're leading the charge. Genpact's AI Gigafactory, our industry-first accelerator, is an example of how we're scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale.

If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what's possible, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions – we help companies across industries get ahead and stay ahead.

Powered by curiosity, courage, and innovation, our teams implement data, technology, and AI to create tomorrow, today.

Responsibilities:

• Use Case Development & Solution Design: Identify, prioritize, and develop high-value analytics use cases across retail functions such as demand forecasting, price & promotion optimization, inventory planning, customer segmentation, and store performance analytics. Translate business needs into analytical problem statements, selecting the right methodologies and data sources. Build business cases, quantify ROI, and work with stakeholders to ensure adoption.
• Advanced Modeling & Analytical Execution: Develop, validate, and deploy machine learning/statistical models (e.g., time-series forecasting, recommendation systems, propensity models, optimization algorithms). Apply financial modeling and accounting understanding to design solutions that tie directly to revenue, margin, cost, and profitability metrics. Conduct exploratory data analysis, feature engineering, and model performance tracking.
• Data Architecture & Engineering Collaboration: Partner with data engineering teams to define data requirements, improve data quality, and optimize pipelines for scalable model deployment. Work within cloud environments (AWS, GCP, Azure) to operationalize models and automate analytics workflows.
• Business Partnership & Communication: Present insights and model outcomes to senior executives in Finance, Operations, and Commercial teams. Build strong relationships with business units to enable data-driven decision-making. Conduct workshops and analytics roadmapping sessions with functional leaders. Implement best practices in model governance, version control, monitoring, and documentation. Lead continuous improvement initiatives to refine models, data processes, and solution performance.
• Delivery Leadership & Program Management: Oversee the delivery of multiple concurrent analytics and data science projects from scoping to deployment. Manage timelines, budgets, quality standards, and stakeholder expectations for all analytics deliverables. Establish governance frameworks, delivery methodologies, and best practices for analytics program execution.
• Technical & Analytical Oversight: Guide data scientists, analysts, and engineers in developing advanced analytical models (e.g., predictive modeling, optimization, NLP, computer vision). Ensure quality, scalability, and interpretability of analytics outputs. Champion the use of modern data science tools, AI/ML platforms, and cloud-based analytics ecosystems (Azure, AWS, or GCP). Lead, mentor, and inspire cross-functional analytics teams to deliver excellence. Foster a culture of innovation, collaboration, and continuous learning. Support hiring, training, and career development of analytics talent.
• Strategic Impact & Innovation: Identify and prioritize high-value use cases across the CGR lifecycle — such as demand forecasting, trade optimization, portfolio mix analysis, consumer segmentation, and digital shelf analytics. Drive adoption of advanced analytics solutions across the organization to unlock revenue growth and operational efficiency. Stay abreast of emerging technologies and trends in AI, GenAI, and data science relevant to the CGR domain.

Qualifications:

• Master's or PhD in Data Science, Statistics, Computer Science, Economics, Finance, Engineering, or related field.
• Relevant years of experience in data science, advanced analytics, or machine learning roles.
• Demonstrated experience developing retail analytics use cases (e.g., demand forecasting, price optimization, customer analytics, churn prediction, assortment planning).
• Strong knowledge of finance, accounting fundamentals, and P&L drivers.
• Expertise in Python or R, SQL, and ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost).
• Experience working with cloud platforms (AWS/GCP/Azure) and modern data stacks.
• Strong understanding of data modeling, version control (Git), and model deployment practices.
• Exceptional communication skills, with the ability to translate complex analytics into clear business recommendations.

Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation.