Specialist

Carrier — Capital Cyberscape, 2nd Floor, Ullahwas, Sector 59, Gurugram, Haryana 122102

Role: Specialist - Spend Analytics Location: Gurgaon Full/ Part-time: Full Time Build a career with confidence Carrier Global Corporation, global leader in intelligent climate and energy solutions is committed to creating solutions that matter for people and our planet for generations to come. From the beginning, we've led in inventing new technologies and entirely new industries. Today, we continue to lead because we have a world-class, diverse workforce that puts the customer at the center of everything we do. About the role The Specialist – Spend Analytics will be responsible for transforming procurement spend data into actionable cost intelligence to drive insights and strategic decision-making related to procurement and cost optimization, generating actionable intelligence for the Global and Regional supply chain teams to achieve financial goals and improve overall operational efficiency. The role combines rigorous spend analytics with deep should-cost expertise — leveraging tools such as aPriori to build fact-based cost models that challenge supplier pricing, identify savings opportunities, and support high-stakes negotiations. A working knowledge of AI and machine learning techniques for spend analytics, anomaly detection, and pattern recognition will be a strong advantage, enabling the specialist to continuously elevate the analytical maturity of the COE and deliver commercially impactful outcomes at scale. Key Responsibilities: A. Spend Data Analysis & Interpretation Conduct in-depth analysis of spend data to identify trends, patterns, and anomalies across categories and suppliers. Utilize statistical techniques and data visualization tools to transform complex datasets into clear, meaningful insights for stakeholders. Leverage tools such as Python, R, VBA, and advanced Excel to build scalable analytical workflows and automate repetitive data processing tasks. Apply machine learning techniques to enhance spend classification, outlier detection, and predicti

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