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Description

Have you ever wondered how Amazon selects the offer(s) for a given product from potentially hundreds of sellers selling the same item? It is one of the most critical models in Amazon and one of the most visible ones on the web. Whenever you see price, anywhere on Amazon, in any locale, it is calling this model at the backend. This is an opportunity to work with the team, Featured Merchant Algorithm, that develops and maintains this model. It drives one of the most coveted real estate in the e-commerce industry, the “Offer Display” across all surfaces (mobile app, mobile web, desktop, Alexa shopping) worldwide. The team's vision is to simplify Amazon shopping experience by helping customers discover and evaluate offers to find the right option for their shopping journey. We use Machine Learning models to select, rank and feature the most relevant offers to customers. Our model serves hundreds of millions of Amazon customers, handles millions of requests per second for hundreds of millions of products and billions of offers across the world.

We are looking for an Applied Scientist to join this high impact, high visibility team. You will lead the development and expansion of the next generation of the featured offer selection models. These new models will incorporate the continually changing preferences of our customers and continue to scale with numerous new programs that Amazon is introducing for our customers. You will work with multiple Amazon businesses and programs to identify big business opportunities and propose new business features and technical systems to improve customer experience. As a team, we own the end-to-end life cycle of models – estimation, simulation and experimentation. We work in the interface of Artificial Intelligence, Statistics and Econometrics. Your work will cut across various sub-disciplines like Causal Inference, Bayesian Modeling, Deep Learning, Counterfactual Estimation and Evaluation, Multi-armed Bandits, and High-dimensional Multivariate Experiments. You will be responsible for the quality of the model and will get the opportunity to present your ideas and share results of your deliverables with Amazon executives and Amazon scholars on a regular basis. As a senior scientist, you will be collaborating with junior scientists to define and enforce broad, company-wide technical standards in statistical modeling, optimization and simulation techniques.

Why is this a great opportunity?

• We impact the global Amazon retail business: We are at the center of Amazon's retail universe. As a part of the broader Buying Experience org, we work closely with other Retail teams like Search, Pricing, Cart, Checkout, Delivery Experience, Ultra Fast Grocery, Subscribe-n-Save, etc. We build systems that are used every time a customer sees any item on the Amazon website (globally) and help them in making purchase decisions. As a result, we constantly strive to earn and preserve the customer trust. Our impact is typically measured in hundreds of millions of dollars.

• We are diverse: Our team is diverse in terms of expertise (SDE/Scientists/Economists/Data Engineer/Product Manager), nationality (10 countries), experience (college graduate to industry veteran), tech-stacks (AWS, Datapath), and office locations (Seattle, Bangalore, Berlin, Vancouver).

• We prioritize learning: Work alongside some of Amazon’s smartest engineers, scientists, economists, and product managers. We have one Principal Economist and two Principal SDE in the team to learn from. We regularly take ML courses and present/teach at internal and external forums. We innovate, publish, and file patents. We regularly review our models with academics like Michael Jordan, Guido Imbens, and Alberto Abadie.

• We ensure work-life balance: Our team works together to provide work-life harmony for all team members. We recognize that the circumstances of our team members vary, and we balance work across the team so that we are all able to maintain high standards on behalf of our customers, while at the same time allowing for rich and happy personal lives.

• We have fun: We find ways to relax and unwind with team events and group lunches.

If you are ready to truly make an impact on a product that is used by hundreds of millions of people around the world, including your own friends and family, then we would love to talk to you.

Key job responsibilities

• Influence: Drive the scientific vision of the team. Present ideas to the senior leadership, build consensus, lead cross-org collaborations, and mentor junior engineers and scientists.

• Execution: Deliver tactical solutions with strategic thinking to create reusable scientific components, resolving immediate challenges while building for future scalability.

• Knowledge: Keep abreast of the trends in academia and industry – know the state-of-the-art. Publish papers and file patents to contribute to the broader scientific community inside and outside the company.

• Scientific complexity: Innovate and extend scientific methods to address customers’ needs or business problems.

• Engineering complexity: Partner with engineering to transform complex technical requirements into production solutions while optimizing performance tradeoffs.

• Technical judgement: Be a pragmatic problem solver, applying judgment and experience to balance the technical trade-offs and how the applied scientific solution is positioned with respect to the state-of-the-art.

Basic Qualifications

• PhD, or Master's degree and 6+ years of applied research experience

• 3+ years of building machine learning models for business application experience

• Experience with neural deep learning methods and machine learning

• Experience programming in Java, C++, Python or related language

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

• Experience with large scale distributed systems such as Hadoop, Spark etc.

• Experience working in the interface of Machine Learning and Causal Inference.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.