Level

AmazonPosted 1mo ago

Business Research Analyst - I, RBS Tech

Business Research Analyst - I, RBS Tech at Amazon scores 93 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengalurufull-time

AI in this role

prompt-engineering
As a Level 4 Research Analyst, you will implement classical ML models and LLM based inferences to solve defined problems. You'll work closely with senior scientists and engineers, contributing to model development, evaluation, and optimization.



Key job responsibilities
* Implement machine learning models (classical ML covering classification and regression models) using Python and ML frameworks
* Develop and iterate prompts for LLMs using few-shot, CoT, and instruction-based techniques
* Conduct offline evaluations and benchmarking / testing on LLM and ML outputs
* Collaborate with engineers to deploy models in production and monitor performance
* Document experiments and results; participate and brief milestones in weekly sprint planning and model reviews

Expectations

* Demonstrates hands-on skills in Python and ML/LLM toolkits
* Understands trade-offs between Generative AI vs. Machine Learning approaches
* Can deliver scoped ML components with guidance
* Effective communicator and team collaborator



About the team
The Retail Business Systems (RBS) group is an integral part of Amazon online product lifecycle and buying operations. The team is designed to ensure Amazon remains competitive in the online retail space with the best catalog quality, wide selection, supply chain defects and compliance programs. The team’s primary role is to create and enhance retail selection on the worldwide Amazon online catalog. The tasks handled have a direct impact on customer buying decisions and online user experience.

Basic qualifications

- Bachelor's degree
- Bachelor's degree in Quantitative or STEM disciplines (Science, Technology, Engineering, Mathematics)
- Relevant experience in solving real world business problems using AI/ML or applying GenAI and LLMs through prompt engineering techniques.
- Strong hands-on programming skills in Python, SQL.
- Strong analytical thinking
- Ability to creatively solve business problems, innovating new approaches where required and articulating ideas to a wide range of audiences using strong data, written and verbal communication skills
- Ability to collaborate effectively across multiple tech and business teams.
- PREFERRED QUALIFICATIONS
- Diverse experience will be favored eg. a mix of experience across different roles - In-depth understanding of machine learning concepts including developing models and tuning the hyper-parameters, as well as deploying models and building ML service - Technical expertise, experience in AI/ML and GenAI.

Preferred qualifications

- Knowledge of data modeling and data pipeline design
- Experience with statistical analysis, co-relation analysis

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt Engineering

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How would you decide a model or AI system is ready to ship?
  3. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Prompt Engineering. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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