We detect and prevent fraud among hundreds of millions of e-Commerce transactions in different countries. We create a trusted marketplace where millions of buyers and sellers can safely transact online. What kinds of processes and systems would you build to optimize our volume predictions? Amazon is seeking a Sr Program Manager in volume forecasting, who will be responsible for i) building business forecasting models through a combination of multiple vectors (baseline trends, systemic adaptation towards change in upstream requirements, project injects), ii) developing strategies for cost optimizing inspection models on the long term predictions, iii) deliver through innovative ideas in a fast paced environment, while enabling downstream teams to achieve Service Level delivery at >90%. This is your chance to make history. We value your passion to discover, invent & simplify, leverage AI in the planning models and deliver for a high performing organisation. Amazon hires the brightest minds, are you one of them? We believe passionately, that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. Key job responsibilities A blend of strategic and execution-focused personnel to own end-to-end Volume Forecasting for Global Verifications and Risk Management Ops [VRMO]. This role, being an individual contributor will guide a set of Program Managers and Analysts responsible for forecasting, capacity planning inputs, programmatic inspection, while contributing to a continuous improvement in mechanisms across a global portfolio of programs across verifications, compliance and risk management operations. This individual contributor thrives in high-ambiguity environments, brings structure to complexity, builds durable mechanisms, and drives forecasting excellence through advanced modelling, rigorous audits, and fast corrective action. KRAs include: 1. Global Volume Forecasting Ownership > Own short, mid, and long-term volume forecasting across a global programs. > Design and continuously improve forecasting models using advanced statistical and predictive techniques. > Translate demand signals into actionable capacity planning and WFM inputs for downstream teams to consume. > Partner cross-functionally (Product, Compliance, Data Science, Operations, Finance, WFM) to ensure forecast alignment and adoption. 2. Mechanism-Driven Execution > Build scalable, auditable forecasting and inspection mechanisms. > Establish weekly/monthly business reviews, variance analysis frameworks, and fast correction loops. > Drive structured problem-solving and root cause deep dives. > Simplify complex interdependencies into clear, repeatable systems > Elevate analytical rigor, business judgment, and executive communication across stakeholders of all levels > Create a culture of ownership, inspection, and data-backed decision making. 3. Dealing in Ambiguity > Navigate rapidly shifting priorities, incomplete data, and evolving business models. > Swiftly adapt forecasting approaches to changing inputs. > Provide clear executive-level narratives in uncertain environments. > Balance long-term structural improvements with short-term execution needs 4. Inspection, Audit & Corrective Action > Proactively detect forecast risks, performance deviations, and weaknesses in established frameworks > Regularly inspect models, assumptions, and upstream inputs, challenge as needed > Implement swift corrective actions with measurable impact, while influencing with limited authority > Drive continuous improvement cycles across global teams. A day in the life The individual contributor must operate with a mental model of 3 broad pillars: i) Today’s variance, ii) Next quarter’s risk, iii) Next year’s scalability. Top 5 - 'day in the life of this Snr PM' is: 1. Inspect Forecast Accuracy & Variance Signals (MAPE, bias, drift, variance in data pipelines) 2. Audit Assumptions & Input Signals, maintaining a visible, dynamic risk register (capacity gaps, data quality and input risks, system fragility around baseline trends) 3. Deep Dive into one forecasting model each day, and challenge - if it's scalable? If it's auditable? Is it reducing upstream dependencies? 4. Engage with cross-functional stakeholders/leaders, driving forecast adoption, and downstream usability 5. Drive clarity in executive narratives - what, why, how, and and what if, followed by decision required for trade-off About the team VRMO - Our globe of Operations {Verification & Risk Management Ops]: Here, we verify identity of all the selling partners who sells on Amazon. We comply with the Amazon Pay regulatory requirements through our verification bar and we proactively detect bad actors and eliminate them from our ecosystem. SWOTS - Strategic Workforce Optimization & Tactical Solutions: A bouquet of support services enabling the VRMO (and SPIV) organization through operational excellence owning 6 key capabilities i) Forecasting, ii) HC Planning, OPEX Budgeting (GPSS) and WFM, iii) Data Security, iv) BCP, v) 3P Outsourcing and Vendor cost governance and vi) Tech-Triaging. ### Basic qualifications - 7+ years of developing program strategies and plans, diving execution, and influencing senior stakeholders experience - 7+ years of data visualization and reports tools experience - Knowledge of Excel at an intermediate level (e.g., pivot tables & charts, multiple criteria lookups, nested logical/IF formulas, data cleansing, array formulas, etc.) - Bachelor's degree in BI, finance, engineering, statistics, computer science, mathematics, finance or equivalent quantitative field - 8–12+ years of experience in advanced forecasting methodologies (e.g., time-series models, regression, scenario modelling, predictive analytics). - Demonstrated experience managing global portfolios and proven ability to operate effectively in high-ambiguity, fast-changing environments. ### Preferred qualifications - Master's degree, or MBA in BI, finance, engineering, statistics, computer science, mathematics, finance or equivalent quantitative field - 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience - Experience building forecasting systems, with familiarity with statistical tools (R, Python, SQL, advanced Excel, BI tools). 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