Data Scientist, Demand Planning and Workforce Intelligence

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Data Science

This role focuses on building Generative AI and LLM-powered solutions for forecasting, operational dashboards, and workforce optimization. It involves creating agentic AI systems for natural language querying, root cause analysis, and recommendation engines, utilizing transformer architectures and prompt engineering.

What you'd actually do

  1. modeling complex problems, discovering insights and identifying opportunities through the use of statistical, machine learning, algorithmic, data mining and visualization techniques, with a strong emphasis on leveraging Generative AI and Large Language Models (LLMs) to drive innovation.
  2. develop and deploy Gen AI-powered solutions for intelligent forecasting, automated pattern recognition in variance analysis, and conversational AI interfaces for operational dashboards.
  3. building agentic AI systems that enable natural language querying, automated root cause analysis, and intelligent recommendation engines for workforce optimization and resource planning.
  4. apply a breadth of tools, data sources and analytical techniques—including transformer architectures, foundation models, and prompt engineering—to answer a wide range of high-impact business questions and present the insights in concise and effective manner.
  5. independently driving issues to resolution and communicating insights to non-technical audiences.

Skills

Required

  • 1+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
  • 1+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources experience
  • 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience

Nice to have

  • Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
  • Knowledge of statistical packages and business intelligence tools such as SPSS, SAS, S-PLUS, or R
  • Knowledge of machine learning concepts and their application to reasoning and problem-solving
  • Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)
  • Experience working with or evaluating AI systems
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions
  • Experience effectively communicating complex concepts through written and verbal communication

What the JD emphasized

  • Generative AI
  • Large Language Models (LLMs)
  • agentic AI systems
  • natural language querying
  • automated root cause analysis
  • intelligent recommendation engines
  • transformer architectures
  • foundation models
  • prompt engineering

Other signals

  • Generative AI
  • Large Language Models (LLMs)
  • agentic AI systems
  • natural language querying
  • automated root cause analysis
  • intelligent recommendation engines
  • transformer architectures
  • foundation models
  • prompt engineering