Business Analyst I, Gar Qc and Governance

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

This role focuses on building AI-driven operational strategies and automations for risk mitigation in financial collections. It involves developing and deploying end-to-end AI solutions, from data ingestion to monitoring, and integrating AI outputs into automated workflows. The role requires strong data analytics, SQL, Python, and experience with AWS services and AI/ML libraries for predictive analytics, anomaly detection, and forecasting.

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 based risk automation, quality control, anomaly detection, or defect prediction using self-serve AI tools
  • AI-powered automation tools and frameworks
  • data modeling
  • ETL
  • data warehousing
  • data lakes
  • specialist-level SQL proficiency
  • API integrations for reading data from applications and feeding into AI/LLM for insights/actions
  • AI/ML libraries for business analytics
  • Excel
  • Access
  • Oracle
  • Essbase
  • SQL
  • VBA
  • engineering and operations best practices (version control, data quality/testing, monitoring)
  • data presentation skills

Nice to have

  • RPA development using UiPath
  • natural language processing (NLP) for text analytics
  • statistical modeling
  • predictive analytics platforms
  • AWS cloud services (SageMaker)
  • AI/ML frameworks and libraries (scikit-learn, TensorFlow, PyTorch)
  • Data visualization tools (QuickSight, Tableau, Power BI)
  • Statistical analysis
  • predictive modeling
  • ETL processes
  • data pipeline development
  • collections, accounts receivable, or financial operations quality assurance

What the JD emphasized

  • Develop end-to-end AI solutions — data ingestion → model training → testing → deployment → monitoring
  • AI-driven operational strategies
  • AI-powered Risk mitigation automations
  • AI-driven text mining
  • AI/ML techniques
  • internal AI tools/MCP
  • AI automation

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