Manager / Senior Manager, Analytics Consulting (Retail/CPG)

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning and AI. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for a business-first, analytically strong consultant who can work directly with business leaders to understand complex decisions, challenge conventional thinking, and rapidly translate business needs into data- and AI-enabled solutions. This is not a traditional requirements-gathering role. The consultant will be expected to understand how the business operates, identify the decisions that matter, determine what information and analysis are needed, and rapidly mobilize AI/Agentic tools, data and specialist teams to solve the problem. The ideal candidate has operated in an environment where they have regularly used data and analysis to support business decisions and is excited about learning and applying emerging AI technologies.

Responsibilities Understand the business and the decision: Engage business stakeholders to understand objectives, economics, processes, constraints and the decisions they are trying to make—not simply document stated requirements. Challenge and structure problems: Ask the right questions, challenge assumptions and convert ambiguous business issues into clear hypotheses, analyses and decision frameworks. Use data to develop insights: Explore and interpret data, conduct rapid analyses, identify patterns and translate findings into business implications and recommendations. Rapidly prototype solutions: Use GenAI, Agentic AI, analytics and low-code/no-code tools to rapidly explore ideas, analyze information and develop working prototypes. Orchestrate expertise: Recognize when deeper expertise is required and effectively leverage data scientists, engineers, domain experts, product teams and AI agents rather than attempting to build everything independently. Drive from insight to action: Communicate findings in simple business language, recommend actions and work with stakeholders to translate recommendations into measurable business outcomes. Continuously learn: Rapidly experiment with emerging AI and technology capabilities and identify where they can materially improve business decision-making.