New opportunity

Data Scientist II, Seller Fee Science

Amazon · Bengaluru, India

About this role

The Seller Fee Science Team at Amazon integrates economic modeling, machine learning, and artificial intelligence to guide fee strategy, quantify impact, and ensure accurate fee computation for billions of transactions. As a Data Scientist II, you will apply data analysis, econometrics, and AI/ML to measure and predict Amazon's P&L, specifically focusing on fee revenue. You will translate ambiguous business challenges into well-defined scientific problems and deploy models that influence global-scale operations. Key Responsibilities: Translate business challenges into scientific problems; improve fee revenue measurement, prediction, and planning; design and deploy econometric or AI/ML models; partner with finance and strategy teams to communicate results and productionalize solutions; apply simulation methods to validate models; and contribute to the scientific community through publications and technical artifacts. Required Qualifications: 2+ years of data scientist experience; 3+ years of experience with data querying languages (SQL), scripting (Python), or statistical software (R, SAS, Matlab); 3+ years of experience with machine learning/statistical modeling tools and techniques; 1+ years of experience guiding/coaching researchers; 1+ years of experience evaluating AI systems; 1+ years of experience creating mathematical/research content; and a Master's degree in a STEM field. Preferred Qualifications: Ph.D. in a STEM field; experience in a ML or data scientist role at a large technology company; experience defining benchmarks for GenAI model performance; and experience working on cross-disciplinary projects.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

SQLPythonRSASMatlabMachine LearningStatistical ModelingEconometricsData AnalysisSimulation MethodsPerl

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Translating Ambiguous Business Needs into Scientific Models
The role requires converting high-level business challenges regarding fee revenue and P&L into well-defined, actionable scientific problems. You must demonstrate the ability to bridge the gap between abstract financial strategy and concrete econometric or AI/ML model design.
Productionalizing Scalable Solutions
Beyond model development, you are expected to deploy solutions that operate at global scale. This requires a focus on the end-to-end lifecycle, including partnering with finance and strategy teams to ensure models are robust, validated through simulation, and integrated into production systems.
Mentorship and Scientific Contribution
As a Data Scientist II, you must actively guide and coach researchers while contributing to the broader scientific community. This involves creating technical artifacts, research papers, or educational content, reflecting a requirement for both technical leadership and knowledge sharing.

Likely challenges to think through

Balancing Theoretical Rigor with Business Velocity
The team integrates complex economic and AI models to influence real-world P&L. A key challenge is maintaining the necessary theoretical depth for accurate fee prediction while meeting the fast-paced, high-impact demands of Amazon's global operations. Prepare by highlighting examples where you successfully simplified complex models for business stakeholders without sacrificing scientific integrity.
Validating Models for High-Stakes Financial Impact
Because your work directly influences fee revenue and seller experience, the margin for error is low. You will face the challenge of designing rigorous simulation methods to validate models before they impact billions of transactions. Prepare by discussing your approach to stress-testing models and quantifying uncertainty in high-stakes environments.

Practice your answers

Technical practice

Translating Ambiguous Business Challenges

What to coverDescribe your process for taking a high-level business goal, such as optimizing fee structures, and decomposing it into a well-defined scientific problem. Focus on how you identify key variables, define success metrics, and determine the appropriate modeling approach.

Econometric vs. Machine Learning Modeling

What to coverWhen predicting fee revenue or P&L impacts, how do you decide between using an econometric model versus a machine learning approach? Discuss the trade-offs regarding interpretability, causal inference, and predictive accuracy in a production environment.

Simulation Methods for Model Validation

What to coverExplain how you use simulation methods to validate models before deployment. Discuss how you design these simulations to account for real-world complexity and quantify potential business impact at scale.

Evaluating AI System Performance

What to coverGiven your experience evaluating AI systems, what metrics and methodologies do you prioritize to ensure model reliability? Address how you handle potential biases or performance degradation when models are applied to billions of transactions.

Benchmarking GenAI Models

What to coverIf tasked with implementing GenAI for fee-related inquiries, how would you define benchmarks to assess performance? Focus on the specific challenges of measuring accuracy, latency, and relevance in a high-stakes financial context.

Productionalizing Scientific Solutions

What to coverDescribe the steps you take to transition a research-grade model into a production-ready system. Highlight your experience collaborating with engineering or finance teams to ensure scalability and operational stability.

Data Querying and Feature Engineering

What to coverDiscuss your approach to handling large-scale datasets using SQL or Python for feature engineering. Explain how you ensure data quality and consistency when dealing with complex, multi-source transaction data.

Statistical Modeling at Scale

What to coverHow do you manage the performance parameters of statistical models when scaling to billions of transactions? Discuss techniques for optimizing model training and inference to meet latency requirements.

Behavioural practice

Guiding and Coaching Researchers

What to coverProvide an example of a time you mentored or coached a researcher. Focus on how you helped them overcome a technical hurdle or improved their research methodology, and what the ultimate outcome was.

Cross-Disciplinary Collaboration

What to coverDescribe a situation where you had to work with non-technical stakeholders, such as finance or strategy teams, to solve a business problem. How did you communicate complex scientific concepts to ensure alignment and buy-in?

Handling Conflicting Priorities

What to coverTell me about a time you had to balance the need for theoretical rigor with the pressure of a tight business deadline. How did you decide what to prioritize and how did you manage stakeholder expectations?

Contributing to the Scientific Community

What to coverShare an example of how you have contributed to the scientific community through publications, talks, or technical artifacts. Explain why you chose that specific topic and how it impacted your peers or the broader field.

Sharpen your knowledge

Topics to prepare

Econometric Modeling for P&L Prediction
The role requires integrating economic modeling with machine learning to predict fee revenue and P&L impact. Exercise: Review literature on causal inference and structural modeling to understand how to isolate the impact of fee changes on seller behavior versus external market factors.
Simulation Methods for Model Validation
The team uses simulation to quantify business impact at scale. Exercise: Practice designing Monte Carlo simulations to stress-test a predictive model against various market volatility scenarios to ensure robustness before production deployment.
AI System Evaluation and Benchmarking
The role involves evaluating AI systems and defining benchmarks for performance, including GenAI. Exercise: Develop a framework for evaluating a predictive model's performance beyond standard metrics, focusing on business-specific KPIs and potential edge-case failures.

Skills to revise

Advanced SQL and Data Pipeline Design
You must handle billions of transactions. Exercise: Optimize complex SQL queries involving large-scale joins and window functions to aggregate financial data efficiently, focusing on query execution plans and performance tuning.
Production-Grade Python Modeling
The role requires deploying models into production environments. Exercise: Refactor a prototype machine learning script into a modular, production-ready Python package, incorporating unit tests, logging, and error handling for data pipeline integration.
Technical Communication for Cross-Functional Stakeholders
You will partner with finance and strategy teams to translate scientific results. Exercise: Draft a non-technical summary of a complex econometric model's findings, explaining the business implications and limitations to a hypothetical finance stakeholder.

Your preparation checklist

Prepare a case study on translating business ambiguity into scientific models
Select a past project where you converted a vague business objective into a formal econometric or machine learning problem. Prepare to explain your choice of methodology, how you handled data constraints, and how you validated the model's impact on P&L or revenue metrics.
Develop a technical walkthrough of your simulation and validation methods
Be ready to discuss how you use simulation methods to stress-test models before production. Focus on how you quantify business impact at scale and ensure your models remain robust when applied to complex, real-world transaction data.
Synthesize examples of cross-disciplinary collaboration and mentorship
Prepare specific anecdotes demonstrating your experience coaching junior researchers and partnering with non-technical stakeholders like finance or strategy teams. Highlight how you communicated complex scientific findings to influence high-level business decisions.
Review your experience with AI system evaluation and GenAI benchmarking
Reflect on your experience evaluating AI systems and defining performance benchmarks. Be prepared to discuss how you measure the efficacy of AI models and your approach to ensuring accuracy in automated decision-making systems.
Formulate strategic questions for the interviewers
Prepare questions regarding the team's current approach to balancing profitability and growth, the specific challenges of modeling fee revenue at Amazon's scale, and how the team integrates theoretical research into production-grade systems.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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