Business Analyst I

Amazon Amazon · Big Tech · IN, TS, Hyderabad · Finance & Accounting

This Business Analyst role focuses on leveraging AI/ML for risk mitigation, predictive analytics, and automation within Amazon's FinOps Collections Quality Control and Governance team. The role involves developing and deploying end-to-end AI solutions, from data ingestion to monitoring, and integrating AI outputs into automated workflows. It requires strong SQL and Python skills, experience with AWS services, and familiarity with AI/ML libraries and NLP for text analytics.

What you'd actually do

  1. Develop moderately to highly complex data processing jobs using SQL, Python, and other technologies
  2. Leverage artificial intelligence and machine learning algorithms for predictive analytics, anomaly detection, and pattern recognition in quality control data
  3. Apply AI-driven text mining and data analytics to identify critical business insights and optimize operational efforts
  4. Build statistically robust forecasting models using AI/ML techniques for operational effort drivers and related metrics
  5. Build Risk identification, monitoring and automated mitigation actions using internal AI tools/MCP.

Skills

Required

  • SQL
  • Python
  • AWS services (Lambda, S3, Glue, Redshift, QuickSuite, EventBridge, Step Function)
  • AI/ML libraries (scikit-learn, TensorFlow, PyTorch, etc.)
  • Data visualization tools (QuickSight, Tableau, Power BI)
  • Statistical analysis and predictive modeling
  • ETL processes and data pipeline development
  • API integrations
  • Excel
  • Access
  • Oracle
  • Essbase
  • VBA
  • RPA development using UiPath
  • NLP for text analytics

Nice to have

  • AWS SageMaker
  • continuous improvement projects

What the JD emphasized

  • Develop end-to-end AI solutions — data ingestion → model training → testing → deployment → monitoring
  • Experience with natural language processing (NLP) for text analytics
  • Familiarity with statistical modeling and predictive analytics platforms

Other signals

  • Leverage artificial intelligence and machine learning algorithms for predictive analytics, anomaly detection, and pattern recognition in quality control data
  • Apply AI-driven text mining and data analytics to identify critical business insights and optimize operational efforts
  • Build statistically robust forecasting models using AI/ML techniques for operational effort drivers and related metrics
  • Build Risk identification, monitoring and automated mitigation actions using internal AI tools/MCP.
  • Develop end-to-end AI solutions — data ingestion → model training → testing → deployment → monitoring
  • Collaborating with Product, Software Engineers and Data Engineers to integrate AI outputs into automated workflows
  • Experience with natural language processing (NLP) for text analytics
  • Familiarity with statistical modeling and predictive analytics platforms