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Machine Learning Scientists

We have ambitious plans for building machine learning platforms and applications and need great machine learning scientists, problem solvers, and leaders who can pull off big long-term strategy.

AmazonCompany: Amazon
Location: Bangalore, India

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Machine learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. We were pioneers in areas such as recommendation engines, eCommerce fraud detection, large scale optimization of fulfillment center operations, etc. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded:

1. Amazon is by far THE most fertile source of DATA for machine learning innovation.
We have perhaps the richest, most diverse data for any internet company. For example, we have clickstream, product search, browse, shopping and customer contact data in 10+ countries in the world, the world's largest catalog of products, one of the largest seller marketplaces and offers data, product reviews, seller feedback etc. to name a few. We have pricing, inventory, fulfillment, shipping, and product demand data. We have data on every digital book on Kindle and digital music and video for rent. We have a Groupon like business with Amazon Local. We have a fast growing although small advertising business. We have the cloud computing data on AWS. The list is endless, and it is the richest, most fertile data in the world for exploring machine learning.

2. Amazon is by far THE most fertile place for ALGORITHMIC automation and innovation.
There is a huge opportunity for innovation using Machine Learning and related sciences at Amazon. From ranking product search results and recommending products to predicting ad click probabilities and forecasting product demand to scheduling tasks across diverse workers in Mechanical Turk, we have a very broad collection of practical problems where ML systems can dramatically improve the customer experience, boost revenue, reduce expenses, and drive speed and automation.

3. Amazon has ambitious plans for leveraging Machine Learning in eCommerce.
We are building platforms that incorporate highly scalable implementations of state-of-the-art machine learning algorithms. We also want to put out platforms on AWS for the use of enterprise customers and startups at large like the other products we offer on AWS. We have applications focused groups who are strongly focused on solving business problems, execution and deployment. Examples of machine learning applications we are developing on our platforms include classification and de-duplication of products, web site classification, prediction of the propensity of customers to purchase products, detection of spam reviews, and recommendation of products to sellers.

Clearly, we have ambitious plans for building machine learning platforms and applications at Amazon. For this, we need great machine learning scientists and problem solvers. We need visionaries who can conceive the next generation machine learning products that raise the bar for customer experience. We are looking for leaders in machine learning who can think big, manage the team of Engineers and Scientists, innovate to help pull off this big strategy over the course of multiple years. So let me know if you are interested in the role and I can schedule an informational discussion for you with Rajeev Rastogi (Director of Machine Learning @ Amazon, Bangalore). You can visit to know more about the team.

Major responsibilities

  • Use statistical and machine learning techniques to create scalable solutions for business problems
  • Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes
  • Design, development and evaluation of highly innovative models for predictive learning
  • Work closely with software engineering teams to drive real-time model implementations and new feature creations
  • Work closely with operations staff to optimize various business operations,
  • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
  • Track general business activity and provide clear, compelling management reporting on a regular basis
  • Research and implement novel machine learning and statistical approaches

Basic Qualifications

  • A PhD in CS machine learning, Operational research, Statistics or in a highly quantitative field
  • 6+ years of hands-on experience in predictive modeling and analysis
  • Strong Problem solving ability
  • Good skills with Java or C++, Perl, Python (or similar scripting language)
  • Distributed programming experience is highly recommended
  • 1+ years of experience in using R, Matlab, SAS/SQL or or any other statistical software
  • Communication and data presentation skills

Preferred Qualifications:

  • 5+ years of industry experience in predictive modeling and analysis
  • Distributed programming experience is highly recommended
  • Experience with Oracle in a Linux or UNIX environment is a nice to have.

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