# Business Analysis Manager at Cisco

AI Level 3, AI centrality 65 out of 100. Remote (Bangalore, India).

## Details

- Company: [Cisco](https://jobsbylevel.com/companies/cisco)
- AI level: AI Level 3 (score 65 out of 100)
- Location: Remote (Bangalore, India)
- Posted: October 7, 2026
- Apply: https://jobsbylevel.com/go/7e1bc88e-7e3d-4008-a017-36133331f0d8

## Description

Meet the Team Cisco’s Global Planning organization is transforming how we forecast demand across our portfolio by combining causal AI with deep market, product, and customer intelligence. As part of this mission, the Central Forecasting team applies Causal AI to deliver resilient, transparent, and actionable demand insights for over 10,000 primary and attached hardware Product IDs (SKUs). Our work directly informs operational demand plans, S&OP processes, and financial references across the enterprise, earning recognition as a top five finalist for Process and Technology Innovation in the 2024 Gartner Power of the Profession Supply Chain Awards. We are seeking a Senior Business Analyst to own the complete demand forecast for designated Business Entities. Operating as a crucial bridge between the Data Science and Demand Planning teams, you will evaluate outputs from multi-level causal machine learning models and apply real-world commercial logic to build defensible, highly accurate forecasts. This influential role goes beyond data aggregation, enabling you to actively shape demand strategy, explain variance to leadership, and influence key stakeholders across Supply Chain, Finance, and Business Units. Because this role supports a globally distributed team across the Americas, Europe, and Asia, it requires flexible working arrangements, including regular evening overlap to collaborate effectively with global stakeholders. Your Impact Forecast Ownership & Integration: Own the 12-to-24-month rolling reference unit forecast at the Product ID (SKU) level, along with aggregated bookings-dollar forecasts for your assigned Business Entities, while accounting for New Product Introductions (NPIs), product transitions, attached Product IDs (SKUs), and End-of-Life (EOL) dynamics. Business Intelligence & AI Refinement: Review causal machine learning outputs across aggregate, platform, and Product ID (SKU) levels, applying qualitative business context—such as sales pipeline shifts, market trends, pricing impacts, and customer demand patterns—to adjust raw model outputs where necessary. Causal Model Enhancement & Feature Development: Collaborate directly with the Data Science team to continuously improve forecast accuracy by providing domain expertise, identifying potential model gaps, recommending new feature inputs, and validating domain-specific variables that enhance model outputs. Scenario Planning & Risk Analysis: Establish baseline confidence levels, forecast ranges, and upside or downside scenarios, performing intervention and sensitivity analyses to quantify how external market changes impact target forecasts. Deep-Dive Exploratory Data Analysis: Conduct exploratory analyses across customer segments, sales channels (Enterprise, Service Provider, Commercial, Government), regions, and product lines to identify structural shifts in demand versus short-term volatility. Stakeholder Influence & Communication: Present clear, evidence-based forecast recommendations to Demand Planning, Business Unit leaders, Finance, and Sales, while defending forecast assumptions during Sales and Operations Planning (S&OP) reviews. Continuous Improvement & Root-Cause Analysis: Lead monthly performance reviews comparing model forecasts against final demand plans and actuals, building variance bridges to isolate root causes of error. Minimum Qualifications Bachelor's degree in business Analytics, Supply Chain, Finance, Data Science, Engineering, or a related quantitative field. 7+ years of experience in demand forecasting, business analytics, financial planning, or commercial operations (or 4+ years with a Master's degree). Proven experience managing end-to-end analytical deliverables and presenting findings to executive stakeholders. Strong quantitative modeling skills, with the ability to reconcile complex datasets across product and financial hierarchies. Ability to work flexible schedules, including evening hours, to align with global cross-functional

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