Theodore Vaslioudis, a former intern and full-time Amazon scientist
Theodore Vaslioudis, a former intern and full-time Amazon scientist since February 2020, uses his experiences to help customers gain the greatest value from AWS resources, and his colleagues make the most of working remotely.

From intern to applied scientist: How Theodore Vasiloudis made the transition

The applied scientist offers advice on how he utilized his internship to land a full-time job — and talks about how he and his colleagues won an award along the way.

In the early days of purchase data analysis, a study determined that people often bought diapers and beer together. When Theodore Vasiloudis, then a computer science undergrad, heard that from a professor at the Aristotle University of Thessaloniki, he was intrigued by the correlation: “I found it fascinating that, by aggregating the data of multiple users, you could extract weird and unexpected things like this.”

That course inspired Vasiloudis, today an applied scientist with Amazon Web Services (AWS), to direct his education toward machine learning. He left Greece in 2012 to study at the KTH Royal Institute of Technology in Stockholm, Sweden, which at the time had one of the few master’s programs in Europe dedicated to machine learning. After finishing his thesis on context-aware recommendations, he pursued an industrial PhD while employed at the Swedish Institute of Computer Science (industrial PhD students develop their research projects while working at a company to gain industrial experience).

In the final years of his PhD, Vasiloudis completed two summer internships at Amazon. One of those resulted in the publication of an award-winning research paper, Block-distributed Gradient Boosted Trees. In that paper, Vasiloudis and his colleagues Hyunsu Cho and Henrik Boström described the development of a new algorithm that was able to drastically reduce the communication cost to train massive, sparse datasets.

A full-time Amazon scientist since February 2020, Vasiloudis now uses his experiences to help customers make the best of AWS resources and his colleagues make the most of working remotely. He has even introduced to his team the custom of fika, the Swedish habit of pausing for a cup of coffee in the middle of the day. Each Friday, he and his teammates congregate over a remote coffee break at 3 p.m., which has helped sustain the team’s spirit during the pandemic. We asked Vasiloudis about his internship, what it was like to make the transition to full-time employee, and more.

Q. What made you interested in working at Amazon, and how was your experience as an intern?

With Amazon, you have the opportunity to reach hundreds of millions of people with your work. You can make changes that affect the everyday lives of such a large population. Also, because of the number of Amazon users, you are forced to design algorithms that can actually analyze massive amounts of data. So that's a very interesting challenge for me, to be able to create scalable algorithms that work regardless of the size of the data set.

For my first internship, I worked with Alexa Shopping and we looked into ways to generate realistic data sets to improve the customer’s experience. The second internship was with AWS, where my manager was Vineet Khare, then an applied science manager. There, I worked on how to get gradient-boosted trees to work with massive data sets that contain millions and billions of records, but also millions and billions of features. From that work, in close collaboration with my mentor Hyunsu Cho, we wrote the paper that won the best short paper award at SIGIR 2019.

These were both good experiences, because I got to work on interesting problems. And most importantly, I got to work with great colleagues. We had multiple interns within the team, and that meant that you could share the experience of being a science intern with other PhD students, and support each other through the internship. My full-time colleagues were also very helpful and fun to hang out with outside work as well. So I had a good time, and that's the main reason why I chose to return to Amazon for the full-time role.

One of the things that I definitely learned during my internships was the importance of writing high-quality code.  A common problem when you're writing research code is that you kind of go along without ensuring that everything works in a formal way. Whereas when writing code for a company, you need to prove and ensure that your code will always work regardless of the circumstances. And this is one of the Amazon leadership principles: That we have to insist on the highest standards.

Theodore Vasiloudis poses with the publication that won the best short paper award at SIGIR 2019.
Theodore Vasiloudis poses with the publication that won the best short paper award at SIGIR 2019.

Q. What set apart the paper that won at SIGIR 2019?

Gradient-boosted trees are designed to deal with very large data sets and are one of the most popular machine learning algorithms, widely used in both academia and industry. However, whenever we deal with very large data sets, often we have to use multiple computers.

Imagine you're trying to classify, for example, text. Let's say that this text is somebody’s loan application. If every possible word in this text is a feature, that means there can be millions of features because the vocabulary is practically limitless. So, when you try to share the model training among multiple computers — which can be a hundred, a thousand, or even more — you will very often run into problems because they are all competing for a tiny amount of bandwidth compared to the data set.

Previous systems were not efficient at communicating because they were wasting a lot of bandwidth with redundant information. Many real-world data sets are very sparse. In sparse data sets, most of the features are actually zeroes. Previous systems were still sending those over the network, and they were consuming a lot of unnecessary bandwidth. Whereas if you only send the non-zeroes over the network, then you're actually saving communication costs and bandwidth. That’s the main idea.

Q. How did you go about trying to find a solution for those sparse data sets?

We had two issues to solve: One regarding prediction and another regarding training. You can imagine a data set as a matrix. It has a bunch of rows, which are the records — for example, the loan application documents. And then each of those will have a number of features, which are the words in the document. So, you can have millions of documents, and millions of features as well. In previous systems, they would only partition the data set along the record dimension. They would take a few documents and put them in one computer, a few in another and then do the training and sync.

But if you want to really speed up the process, you can actually take part of a document and store it in one computer and another part in another computer. This is called block distribution. Instead of taking multiple rows from the same matrix, and storing them in the same computer, now we start taking a block — a few rows and a few columns — from that matrix and put it in one computer. That means that we have some additional communication to do to make predictions.

We used an existing algorithm for that called Quickscorer, which was designed for a completely different purpose, to speed up the prediction process locally. But that exact same approach can allow you to perform a very quick distributed prediction, and we modified that algorithm to adapt our use case. So that's how we solved that prediction issue. And then for the training, we did something similar, where we would only send for a given block the number of records that are necessary with a number of features, and then we would use an aggregation step in order to complete the training.

I think this work provides a good direction for future production systems. The communication pattern for very large data sets should be more flexible than the one that is currently used.

Q. What are you currently working on?

I'm working on SageMaker JumpStart. We create AWS solutions that allow customers to get started with SageMaker faster, and take their ideas to production more quickly and painlessly.  One part of my team’s responsibilities is to work directly with customers when they have a specific problem. But we also do a lot of innovating on the behalf of our customers.

Q. You started your full-time role right at the beginning of the pandemic. Did that affect your work in any way?

We stopped going to the office and started using a digital form of communication. In trying to keep the team spirit alive, one of the things that I try to have in our team is something that we used to have in Sweden, which is called the fika. It’s like a coffee break where you stop working for half an hour and chat with your colleagues about anything you want. It’s just some social time where everybody can relax and interact with colleagues.

If you have the opportunity to work at a company like Amazon, you should definitely take it, because you can gain a lot of experience that is impossible to gain during your PhD.
Theodore Vasiloudis

I saw that, with COVID-19, the interaction with colleagues goes down significantly, so it's good to have some time allocated in your calendar when you don’t have to work, just have some coffee and chat. An informal conversation is when a lot of important ideas come up, and it’s good to have that opportunity.

Q. What advice would you give to people considering following your footsteps?

If you have the opportunity to work at a company like Amazon, you should definitely take it, because you can gain a lot of experience that is impossible to gain during your PhD. The way that the industry works is very different from the way academia works. If you have done a couple of these internships, you're much more prepared to join the workforce.

For interns at Amazon hoping to migrate into a full-time job, I would say that the regular check-ins with your hiring manager are very important, because you need to be constantly aware whether you're on track for your full-time offer. Every second week you get to sit with your hiring manager, and you can check with them if you should be doing something more, if you're hitting your targets in terms of the progress of the work itself, and in terms of representing the leadership principles of Amazon in your work. And that gives you a better sense of accomplishment. You need to make sure that you set a few milestones in the meantime and make sure that you hit them as you progress through your internship.

Q. Any final tips on how to make the best out of your internship at Amazon?

How to become an intern at Amazon

If you’re a student with interest in an Amazon internship, you can find additional information here, and submit your details for review. Students can also learn more about internship opportunities at Amazon Student Programs.

Amazon values being independent and self-driven. And it's very good if you have a goal to publish a paper by the end of your internship and chase that publication. For example, we completed the writing of our paper after I had finished my internship, so if I hadn't pushed for that, I wouldn't have published this paper, and my co-authors and I wouldn't have gotten this award.

It's important to be motivated to work with your manager to make sure that you get all the necessary approvals before you finish your internship toward publishing the paper, because it's an important step for a career as a scientist, as well as for a PhD student, to publish high-quality papers. And it's a unique opportunity to do that when you have access to the infrastructure and data sets of Amazon.

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The Amazon Product Catalog group is hiring a Senior Applied Scientist to help us build the most authoritative product knowledge in existence. Our charter is to capture every relevant fact and relationship for any product on the planet. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day, by people you know.As a Senior Applied Scientist, you will be responsible for designing, developing, and deploying large-scale data mining solutions and distributed machine learning systems that ultimately make shopping on Amazon delightful and functional. Because the product catalog is the heart of our retail business, your work will directly change how Amazon customers search, find, compare, and buy everything from televisions to groceries. Our catalog contains hundreds of billions of facts, a scale which demands automated, state-of-the-art techniques to identify defects, abuse, incorrect data, and find new relationships between products, facts, and entities.Our domain blurs the boundaries between knowledge graphs, ontologies, entity recognition, image classification, machine translation, entity recognition, and semantic fact extraction. This requires a fast, collaborative environment We not only leverage best-in-class models, but also improve them. You will have the support of a well-established team with a long-term charter, science-smart engineers, language experts, and the latest AWS products and compute resources. You will collaborate closely with teams of software engineers, applied machine learning scientists, product managers, user interface designers, and others in order to influence our business and technical strategy, and play a key role in defining the team’s roadmap.A successful candidate will have an established background in machine learning science, large scale software systems, a strong technical ability, great communication skills, and a motivation to solve new, ambiguous problems.Unique Opportunities· Global impact on hundreds of millions of Amazon customers· Opportunities to publish both internally and externally· Close-knit partnership with science-smart developers – your improvements can roll out in days, not months, because engineers understand your work· Access to Amazon-scale datasets, spanning hundreds of billions of facts· Support from over 100 dedicated language experts and SMEs to label, validate, and test your hypothesis· Challenging, cutting-edge science problems that cross multiple domains (such as textual, semantic and image-aware Transformer models )Key Responsibilities· Develop production-ready machine learning solutions to improve Amazon Product Catalog· Collaborate with and influence scientists and engineers in multiple teams· Refine existing techniques, optimizing accuracy, throughput, and global impact· Publish results internally and externally
US, WA, Seattle
Want to watch a movie at the end of a long week, but not sure what to choose? Looking for a new show while you wait for the next season of Game of Thrones to start? So are millions of our Prime Video customers. The Prime Video Relevance team helps customers find relevant videos, channels and topics so they can find content they didn’t even known they were looking for, continuing to surprise them with the depth of our catalog.We tailor our recommendations through a variety of machine learning algorithms including deep learning neural networks, that you will help define and extend. We are looking for creative, customer and details obsessed machine learning scientists who can apply the latest research, state of the art algorithms and machine learning to build highly scalable recommendation and personalization systems. You'll have a chance to collaborate with talented teams of engineers and scientists to run these predictions on distributed systems at incredible scale and speed.As a member of the Prime Video Personalization organization, you will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At then of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide.Some examples of the things we work on:· Using Neural Networks and Deep Learning techniques to find titles that customers will enjoy· Build and operate services that deliver millions of recommendations per second· Extend models and algorithms to support our ever growing ways of consuming content (subscriptions, live, rentals etc), dealing with unique challenges such as observational bias and rapidly scaling dimensions· Constantly experimenting with changes to the underlying algorithms and models to deliver relevant content to a wide variety of customer experiencesIf you are ready to truly make an impact on a product that is used by millions of people around the world, including your own friends and family, then we would love to talk to you.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
US, WA, Seattle
Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists and Engineers developing models to predict customer behavior and optimize the customer experience with Amazon Prime. This includes identifying who our customers are and providing them with personalized relevant content. As an ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions.There are numerous scientific and technical challenges you will get to tackle in this role, such as global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, cohort identification in a semi-supervised setting, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from supervised learning, bandits, optimization, and RL.As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Redshift, Sagemaker, DynamoDB, S3, ...), various ML algorithms and techniques (XGBoost, Random Forests, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques.Major responsibilities· · Build and develop machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams.· · Leverage Bandits and Reinforcement Learning for Recommendation Systems.· · Develop offline policy estimation tools and integrate with reporting systems.· · Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.· · Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.· · Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.· · Use machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.· · Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
US, WA, Seattle
Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists and Engineers developing models to predict customer behavior and optimize the customer experience with Amazon Prime. This includes identifying who our customers are and providing them with personalized relevant content. As an ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions.There are numerous scientific and technical challenges you will get to tackle in this role, such as global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, cohort identification in a semi-supervised setting, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from supervised learning, bandits, optimization, and RL.As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Redshift, Sagemaker, DynamoDB, S3, ...), various ML algorithms and techniques (XGBoost, Random Forests, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques.Major responsibilities· · Build and develop machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams.· · Leverage Bandits and Reinforcement Learning for Recommendation Systems.· · Develop offline policy estimation tools and integrate with reporting systems.· · Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.· · Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.· · Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.· · Use machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.· · Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
US, WA, Seattle
Are you excited about powering Amazon’s physical stores’ expansion through the application of Machine Learning and Big Data technologies? Do you thrive in a fast-moving, innovative environment that values data-driven decision making, scalable solutions, and sound scientific practices? We are looking for experienced scientists to build the next level of intelligence that will help Amazon physical stores grow and succeed.Our team is responsible for building the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. We are tackling cutting-edge, complex problems — such as predicting the optimal location for new Amazon stores — by bringing together numerous data assets from disparate sources inside and outside of Amazon, and using best-in-class modeling solutions to extract the most information out of them.You will have a proven track-record of delivering solutions using advanced science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions. You will transform one-off models into automated systems. You will be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.Responsibilities:· Develop production software systems utilizing advanced algorithms to solve business problems.· Analyze and validate data to ensure high data quality and reliable insights.· Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.· Proactively identify interesting areas for deep dive investigations and future product development.· Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.· Leverage industry best practices to establish repeatable applied science practices, principles & processes.
US, WA, Seattle
Global Talent Management (GTM) at Amazon owns a suite of products which helps drive career development for hundreds of thousands of Amazonians across the world. GTM - Science utilizes a wide array of data sources to conduct analytics and create predictive models that fuel recommendations, actions, and insights in nearly a dozen software systems. The team itself is composed of a variety of scientists and engineers with varied backgrounds, coming together to create diverse and innovative solutions to the problems faced by the one of the world’s largest and fastest growing workforces.This role will support the advancement of key workforce planning products owned by the team. The role will be a scientific lead for forecasting in the organization and a thought leader for forecasting applications throughout HR. If you’re interested in building models used regularly by thousands of Amazonians, to inform talent management decisions, this role is for you. You will support interesting, analytical problems, in an environment where you get to learn from other experienced economists and apply econometrics at massive scale.You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.· Build and operationalize econometric and statistical models· Perform model refreshes or updates to analyses as needed· Work collaboratively with economists and research scientists to assist in the design and implementation of analysis to answer challenging HR questions· Interpret and communicate results to outside customers· Aggregate and analyze data pulled from disparate sources (HR, Finance or other business systems) and related industry and external benchmarks; provide insights and a point of view on analysis and recommendations· Assist in the design and delivery of automated, scalable analytical models to stakeholders· Report results in a manner which is both statistically rigorous and compellingly relevant
US, VA, Arlington
Global Talent Management (GTM) at Amazon owns a suite of products which helps drive career development for hundreds of thousands of Amazonians across the world. GTM - Science utilizes a wide array of data sources to conduct analytics and create predictive models that fuel recommendations, actions, and insights in nearly a dozen software systems. The team itself is composed of a variety of scientists and engineers with varied backgrounds, coming together to create diverse and innovative solutions to the problems faced by the one of the world’s largest and fastest growing workforces.This role will support the advancement of key workforce planning products owned by the team. The role will be a scientific lead for forecasting in the organization and a thought leader for forecasting applications throughout HR. If you’re interested in building models used regularly by thousands of Amazonians, to inform talent management decisions, this role is for you. These are exciting fast-paced businesses in which work on extremely interesting analytical problems, in an environment where you get to learn from other experienced economists and apply econometrics at massive scale.You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.· Build and operationalize econometric and statistical models· Perform model refreshes or updates to analyses as needed· Work collaboratively with economists and research scientists to assist in the design and implementation of analysis to answer challenging HR questions· Interpret and communicate results to outside customers· Aggregate and analyze data pulled from disparate sources (HR, Finance or other business systems) and related industry and external benchmarks; provide insights and a point of view on analysis and recommendations· Assist in the design and delivery of automated, scalable analytical models to stakeholders· Report results in a manner which is both statistically rigorous and compellingly relevant