Applied Scientist
Bangalore , KA, IN
2d ago


The Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Search services go to work.

We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day.

Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide.

Amazon has grown rapidly and will continue to do so in foreseeable future. Providing a high quality search experience is a unique challenge as Amazon expands to new customers, countries, categories, and product lines.

We are seeking a strong applied scientists to join the newly formed Relevance India team. This team’s charter is to increase the pace at which Amazon expands and improve the search experience at launch.

In practice, we aim to invent universally applicable signals and algorithms for training machine-learned ranking models and improve the machine-learning framework for training and offline evaluation that is used for all new relevance models.

In this role you will :

  • Build machine learning models for Product Search.
  • Develop new ranking features and techniques building upon the latest results from the academic research community.
  • Propose and validate hypothesis to direct our business and product road map. Work with engineers to make low latency model predictions and scale the throughput of the system.
  • Focus on identifying and solving customer problems with simple and elegant solutions.
  • Design, develop, and implement production level code that serves billions of search requests. Own the full development cycle : design, development, impact assessment, A / B testing (including interpretation of results) and production deployment.
  • Collaborate with other engineers and related teams within and to find technical solutions to complex design problems.
  • Take ownership. Understand the needs of various search teams, distil those into coherent projects, and implement them with an eye on long-term impact.
  • Be a leader. Use your expertise to set a high bar for the team, mentor team members, set the tone for how to take on and deliver on large impossible-sounding projects.
  • Be ambitious. Find and eagerly tackle hard problems.
  • Be curious. You will work alongside systems engineers, machine learning scientists, and data analysts. Your effectiveness and impact will depend on discussing problems with and learning from them.
  • You will have access to the cutting-edge technologies and vast technical tools and resources of Amazon and will need to learn how to use them effectively.

  • Be customer focused. Work backwards from customer problems, figure out elegant solutions, and implement them for speed and scalability.
  • Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.

    com (AMZN), one of the world's leading internet companies. We provide a highly customer-centric, team-oriented environment in our office located in Banglore.


  • Master's degree in CS, Machine Learning, Mathematics, Statistics or in any another highly quantitative field
  • 2+ years of hands-on experience in predictive modeling and analysis
  • 2+ years of experience in using Matlab, R, Python or similar languages
  • 1+ years of experience with Java, C++, or similar languagesSynonyms for "Bachelor'sCS"

    The ideal candidate will have a PhD in Mathematics, Statistics, Machine Learning, or a related quantitative field. We also prefer that the candidate have :

  • Peer reviewed scientific contributions in relevant field.
  • Ability to apply theoretical models in an applied environment.
  • Strong fundamentals on a broad set of ML approaches and techniques.
  • Strong fundamentals in problem solving and algorithm design.
  • Strong interest in learning, researching, and creating new technologies with commercial impact.
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