Business Intelligence Engineer, Operational Risk & Compliance, Amet

Amazon Amazon · Big Tech · IN, HR, Gurugram · Corporate Operations

This role focuses on building data pipelines, creating metrics decks, performing advanced statistical analysis, and measuring the success of classification models. It requires strong skills in SQL, data engineering, statistical analysis (hypothesis testing, A/B testing), and scripting for automation, with experience in information retrieval, data science, and machine learning.

What you'd actually do

  1. Design and implement metrics to measure the success and effectiveness of classification models by understanding the nuances and potential pitfalls.
  2. Use visualization tools and develop data pipelines to publish the metrics to internal and external stake holders
  3. Implementation of various sampling techniques with the ability to handle issues arising from sampling, like sampling biases.
  4. Able to do statistical tests like hypothesis testing, including parametric and non-parametric tests and is familiar with A/B testing.

Skills

Required

  • SQL
  • data engineering
  • statistical analysis
  • hypothesis testing
  • A/B testing
  • frequentist statistics
  • Python
  • AWS technologies
  • information retrieval
  • data science
  • machine learning
  • data mining

Nice to have

  • business stakeholders
  • senior leadership

What the JD emphasized

  • excellent statistical and analytical abilities
  • deep knowledge of business intelligence solutions and data engineering practices
  • proficiency in hypothesis testing
  • familiar with A/B testing
  • strong grasp of frequentist statistics
  • build data pipelines
  • robust metrics decks
  • perform advanced statistical analysis
  • measure the success of our model deployments
  • translating complex data insights into actionable strategies
  • communicate these effectively to both technical and non-technical audiences
  • understand business needs
  • provide data-driven recommendations
  • Ability to clearly articulate assumptions, methodologies, results, and implications.
  • Able to present deep dives and analysis to both technical and non-technical stakeholders, ensuring clarity and understanding.
  • Design and implement metrics to measure the success and effectiveness of classification models by understanding the nuances and potential pitfalls.
  • Use visualization tools and develop data pipelines to publish the metrics to internal and external stake holders
  • Implementation of various sampling techniques with the ability to handle issues arising from sampling, like sampling biases.
  • Able to do statistical tests like hypothesis testing, including parametric and non-parametric tests and is familiar with A/B testing.
  • Experience with theory and practice of design of experiments and statistical analysis of results
  • Experience with theory and practice of information retrieval, data science, machine learning and data mining