Business Intelligence Engineer II, AOP

Amazon; India; On-site; INR 2,500,000 - 5,500,000; Full time

Job summary

Amazon APAC/Ops is seeking a data science professional to own analytics models and experiments end-to-end. You will design robust reporting frameworks, quantify model impact, and collaborate across scientists, engineers, and stakeholders to drive continuous improvements in cost savings and efficiency across large-scale operations.

You will work with SQL, Python, and AWS services to move between deep analytics and clear stakeholder communication, owning outcomes from hypothesis to deployment.

Qualifications

• 3+ years analyzing data with Redshift, Oracle or NoSQL.
• Experience with data visualization using Tableau or Quicksight.
• Experience with data modeling, warehousing and ETL pipelines.
• Experience in statistical analysis packages such as R, SAS and Matlab.
• Experience using SQL to pull data and Python for modeling.

Responsibilities

• Own analytics models and experiments end-to-end for Ops.
• Design automated reporting frameworks and dashboards.
• Evaluate model performance and quantify impact in production.
• Collaborate with scientists and engineers to drive improvements.

Skills

SQL

Python

Statistics

Data Visualization

Machine Learning

Tools

AWS

Redshift

DynamoDB

Tableau

Quicksight

Job description

Amazon strives to be Earth's most customer-centric company where people can find and discover virtually anything they want to buy online. By giving customers more of what they want - low prices, vast selection, and convenience - Amazon continues to grow and evolve as a world-class e-commerce platform. The AOP team is an integral part of this and strives to provide Data and Science Capabilities to fulfill all customer processes in the APAC, LATAM, IN and MENA Ops businesses. This BIE role sits at a rare intersection and has high growth prospects. On one side, you have cutting-edge ML/OR models making split-second cost saving decisions across Ops network. On the other, you have the analytical depth required to understand why those models succeed, where they fall short, and what needs to change. You won't just be observing these models from the outside - you'll own them along with the Scientists. Their performance, their enhancements, their evolution over time - that's your responsibility. When the model needs a new feature, a re-calibrated threshold, or a fundamentally different approach to a particular corridor or condition, you're the one driving that forward in partnership with the scientists who built it. In some instances you will also directly own these models. The technical challenges here are genuinely complex and rewarding. You'll work with large-scale, low-latency data flowing through real-time pipelines. You'll design automated reporting frameworks that need to be as robust as the system they monitor. You'll apply statistical rigor to evaluate model performance in production, design experiments to validate enhancements, and quantify impact in an environment where conditions shift daily. But beyond monitoring, you'll be the one identifying what's broken, proposing what to build next, and owning the analytical path from hypothesis to deployed improvement. This is a role without a single well-worn toolkit, you'll have the opportunity to continuously sharpen your skills across data engineering, statistical analysis, and model evaluation to keep pace with the problems in front of you. What makes this role particularly compelling is the combination of ownership and impact. The products you will work on drive millions in cost savings for Amazon Ops. You will also be involved in cutting-edge research. You'll need to be comfortable with ambiguity. Many of the most important questions in this space haven't been cleanly articulated yet - they emerge from patterns in the data, from model behavior that doesn't match expectations, from edge cases that only surface under certain network conditions. You'll need strong statistical foundations, fluency in SQL and Python and AWS Services, and experience building scalable BI solutions. Your ability to move between deep technical analysis and clear stakeholder communication - and to own outcomes end-to-end rather than just deliver reports - is what will make you exceptional in this role.

About the team

The AOP Science Team is the central science team of Amazon APAC, LATAM, IN and MENA Operations. We are responsible for creating core science capabilities to scale decision making across domains such as Truck Capacity Optimization, Short-term capacity planning, Network Abuse Detection, Impact analyses, Productivity improvement Models and so on. As a team our goal is to support our customers to make better decisions by leveraging applied statistics, machine learning, OR, deep learning architecture and algorithms. Team owns end-to-end life cycle for science model development which includes data exploration, experimentation, automating near real-time data pipelines, data visualization and model productionization.

• 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
• Experience with data visualization using Tableau, Quicksight, or similar tools
• Experience with data modeling, warehousing and building ETL pipelines
• Experience in Statistical Analysis packages such as R, SAS and Matlab
• Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling

• Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
• Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets

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