Author

Georgios Patsakis

Applied Scientist

Latest news

US, WA, Bellevue
Amazon LEO is Amazon's low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon LEO will serve people and organizations operating in locations without reliable connectivity. The Amazon LEO Global Business Operations (GBO) team drives data-driven decision-making across sales, marketing, operations, product, engineering, finance, and legal functions. We build scalable business intelligence solutions and data infrastructure to solve complex, ambiguous problems with LEO-wide impact. We are looking for a talented Research Scientist to contribute to LEO's long-term vision and strategy for capacity simulations and inventory optimization. This effort will be instrumental in helping LEO execute on its business plans globally. As one of our valued team members, you will be obsessed with matching our standards for operational excellence with a relentless focus on delivering results. Key job responsibilities In this role, you will: Collaborate with product, business development, sales, marketing, operations, finance, and various technical teams (engineering, science, R&D, simulations, etc.) to support the implementation of capacity simulations and inventory optimization solutions. Develop and prototype scalable solutions to optimization problems for operating and planning satellite resources. Support technical roadmap definition efforts by building models to predict future inventory availability and key operational and financial metrics across the network. Design experiments and simulations to evaluate optimization improvements and understand how they interact with each other. Analyze large amounts of satellite and business data to identify simulation and optimization opportunities. Communicate insights and recommendations to technical and non-technical audiences to support decision-making across LEO. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Our organization is building world-class teams with deep expertise in large-scale recommender systems. This role sits at the intersection of AI research and direct business impact, where recommendation quality directly influences business outcomes and customer satisfaction. You'll be joining a team focused on foundational models for recommender systems and working on production systems that serve millions of customers and shape the future of personalized entertainment experiences. We're seeking talent who can deliver measurable impact on our core business metrics while advancing the state-of-the-art in personalization and recommendation technology. Key job responsibilities - Develop AI solutions for various Prime Video Search & Recommendation systems using Deep Learning, Reinforcement Learning, Optimization Methods, and most importantly, GenAI - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses - Effectively communicate technical and non-technical ideas with teammates and stakeholders - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team The Prime Video - Personalization & Discovery Science team owns science solution to power search experience on various devices, from sourcing, relevance, & ranking (to name a few). We are on a mission to deliver an AI-first customer experience. At the heart of this transformation are our recommendation systems -- core, customer-facing components that serve as primary drivers of engagement & growth.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
IN, KA, Bengaluru
If you have ever bought or sold anything on Amazon, you have touched Amazon Marketplace. Amazon’s Marketplace business is one of the largest in the world. We are now in 23 countries. We are growing fast, with customers in many more countries. Amazon’s platform is the engine that powers Amazon’s Marketplace businesses, and Sellers rely on this platform and our support to start selling on Amazon and to grow their business. Amazon Marketplace enables millions of Sellers worldwide to list hundreds of millions of products and manage orders for inventory across dozens of different categories and languages. While working with millions of Sellers worldwide, we constantly strive to improve the selection for Customers and the capabilities of our platform for Sellers. The Seller Fulfillment Services (SFS) team is looking for a motivated and innovative Applied Scientist with strong analytical skills and practical experience to join our science team. As a key member of the SFS science team, you will provide expertise that helps accelerate the business. You will build science solutions that will help us to provide our customers with the largest selection of merchants at the lowest, and the most reliable delivery service regardless of the seller. You will research, design and improve on the models that will impact Amazon’s customer directly. You will be working in a highly collaborative environment partnering with various science, product management, engineering, operations, finance, business intelligence and analytics teams to develop science models to solve business problems. You will need to understand the business requirements and translate them into complex analytical outputs. You will design tests to explain performance of the models from impact on customer and cost perspective. You will create ML models to capture features impacting performance. You should be comfortable building prototypes, testing and improving them given the feedback from the real time data. You should be able to present your model and findings to a various range of stakeholders. An ideal candidate will be an expert in the areas of machine learning, operations research, and statistics. With expertise in applying theoretical models in an applied environment relying heavily on the latest advances in machine learning, optimization, stochastic modeling, and engineering. The candidate will be expected to work on numerous aspects, such as feature engineering, modeling, probabilistic modeling, hyper-parameter tuning, scalable inference methods and latent variable models. Challenges will involve dealing with very large data sets and requirements on throughput. Key job responsibilities - Design, implement, test, deploy, and maintain innovative science solutions to accelerate our business. - Create experiments and prototype implementations of new learning algorithms and prediction techniques - Collaborate with scientists, engineers, product managers, and stakeholders to design and implement software solutions for science problems - Use best practices to ensure a high standard of quality for all of the team deliverables
US, WA, Seattle
WW Amazon Stores Finance Science (ASFS) works to leverage science and economics to drive improved financial results, foster data backed decisions, and embed science within Finance. ASFS is focused on developing products that empower controllership, improve business decisions and financial planning by understanding financial drivers, and innovate science capabilities for efficiency and scale. We are looking for a data scientist to lead high visibility initiatives for forecasting Amazon Stores' financials. You will develop new science-based forecasting methodologies and build scalable models to improve financial decision making and planning for senior leadership up to VP and SVP level. You will build new ML and statistical models from the ground up that aim to transform financial planning for Amazon Stores. We prize creative problem solvers with the ability to draw on an expansive methodological toolkit to transform financial decision-making with science. The ideal candidate combines data-science acumen with strong business judgment. You have versatile modeling skills and are comfortable owning and extracting insights from data. You are excited to learn from and alongside seasoned scientists, engineers, and business leaders. You are an excellent communicator and effectively translate technical findings into business action. Key job responsibilities Demonstrating thorough technical knowledge, effective exploratory data analysis, and model building using industry standard ML models Working with technical and non-technical stakeholders across every step of science project life cycle Collaborating with finance, product, data engineering, and software engineering teams to create production implementations for large-scale ML models Innovating by adapting new modeling techniques and procedures Presenting research results to our internal research community
US, NJ, Newark
Employer: Audible, Inc. Title: Data Scientist II Location: 1 Washington Street, Newark, NJ 07102 Duties: Independently own, design, and implement scalable and reliable solutions to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the approach is unclear. Acquire data by building the necessary SQL/ETL queries. Import processes through various company specific interfaces for accessing RedShift, and S3/edX storage systems. Deliver artifacts on medium size projects that affect important business decisions. Build relationships with stakeholders and counterparts, and communicate model outputs, observations, and key performance indicators (KPIs) to the management to develop sustainable and consumable products and product features. Explore and analyze data by inspecting univariate distributions and multivariate interactions, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build production-ready models using statistical modeling, mathematical modeling, econometric modeling, machine learning algorithms, network modeling, social network modeling, natural language processing, large language models and/or genetic algorithms. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. Position reports to Newark, NJ office; however, telecommuting from a home office may be allowed. Requirements: Requires a Master’s degree in Statistics, Computer Science, Computer Engineering, Data Science, Machine Learning, Applied Math, Operations Research, or a related field plus two (2) years of experience as a Data Scientist or other occupation involving data processing and predictive Machine Learning modeling at scale. Experience may be gained concurrently and must include: Two (2) years in each of the following: - Utilizing specialized modelling software including Python or R - Building statistical models and machine learning models using large datasets from multiple resources - Building non-linear models including Neural Nets, Deep Learning, or Gradient Boosting. One (1) year in each of the following: - Building production-ready solutions or applications relying on Large Language Models (LLM), accessed programmatically and beyond just prompting - Evaluating LLM results at scale or fine-tuning LLMs - Building production-ready recommendation systems - Using database technologies including SQL or ETL. Alternatively, will accept a Bachelor’s degree and five (5) years of experience. Salary: $169,550 - 207,500 /year. Multiple positions. Apply online: www.amazon.jobs Job Code: ADBL175.
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As a Senior Applied Scientist on our team, you will focus on building state-of-the-art ML models for healthcare. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform healthcare outcomes. Key job responsibilities In this role, you will: • Design and implement novel AI/ML solutions for complex healthcare challenges • Drive advancements in machine learning and data science • Balance theoretical knowledge with practical implementation • Work closely with customers and partners to understand their requirements • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions • Establish best practices for ML experimentation, evaluation, development and deployment • Partner with leadership to define roadmap and strategic initiatives You’ll need a strong background in AI/ML, proven leadership skills, and the ability to translate complex concepts into actionable plans. You’ll also need to effectively translate research findings into practical solutions. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the Special Projects organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team We represent Amazon's ambitious vision to solve the world's most pressing challenges. We are exploring new approaches to enhance research practices in the healthcare space, leveraging Amazon's scale and technological expertise. We operate with the agility of a startup while backed by Amazon's resources and operational excellence. We're looking for builders who are excited about working on ambitious, undefined problems and are comfortable with ambiguity.
IN, TS, Hyderabad
Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as an Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. The position is based in Seattle but will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Please visit https://www.amazon.science for more information Key job responsibilities - Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the international seller services domain. - Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. - Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. - Continuously explore and evaluate state-of-the-art NLP techniques and methodologies to improve the accuracy and efficiency of language-related systems. - Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact.
US, CA, San Francisco
Amazon AGI Autonomy develops foundational capabilities for useful AI agents. We are the research lab behind Amazon Nova Act, a state-of-the-art computer-use agent. Our work combines Large Language Models (LLMs) with Reinforcement Learning (RL) to solve reasoning, planning, and world modeling in the virtual world. We are a small, talent-dense lab with the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. Come be a part of our journey! -- About the team: We are a research engineering team responsible for data ingestion and research tooling that support model development across the lab. The lab’s ability to train state-of-the-art models depends on generating high-quality training data and having useful tools for understanding experimental outcomes. We accelerate research work across the lab while maintaining the operational reliability expected of critical infrastructure. -- About the role: As a frontend engineer on the team, you will build the platform and tooling that power data creation, evaluation, and experimentation across the lab. Your work will be used daily by annotators, engineers, and researchers. This is a hands-on technical leadership role. You will ship a lot of code while defining frontend architecture, shared abstractions, and UI systems across the platform. We are looking for someone with strong engineering fundamentals, sound product judgment, and the ability to build polished UIs in a fast-moving research environment. Key job responsibilities - Be highly productive in the codebase and drive the team’s engineering velocity. - Define and evolve architecture for a research tooling platform with multiple independently evolving tools. - Design and implement reusable UI components, frontend infrastructure, and APIs. - Collaborate directly with Research, Human -Feedback, Product Engineering, and other teams to understand workflows and define requirements. - Write technical RFCs to communicate design decisions and tradeoffs across teams. - Own projects end to end, from technical design through implementation, rollout, and long-term maintenance. - Raise the team’s technical bar through thoughtful code reviews, architectural guidance, and mentorship.
US, CA, San Francisco
Amazon AGI Autonomy develops foundational capabilities for useful AI agents. We are the research lab behind Amazon Nova Act, a state-of-the-art computer-use agent. Our work combines Large Language Models (LLMs) with Reinforcement Learning (RL) to solve reasoning, planning, and world modeling in the virtual world. We are a small, talent-dense lab with the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. Come be a part of our journey! -- About the team: We are a research engineering team responsible for data ingestion and research tooling that support model development across the lab. The lab’s ability to train state-of-the-art models depends on generating high-quality training data and having useful tools for understanding experimental outcomes. We accelerate research work across the lab while maintaining the operational reliability expected of critical infrastructure. -- About the role: As a backend engineer on the team, you will build and operate core services that ingest, process, and distribute large-scale, multi-modal datasets to internal tools and data pipelines across the lab. This is a hands-on technical leadership role. You will ship a lot of code while defining backend architecture and operational standards across the platform. The platform is built primarily in TypeScript today, with plans to introduce Python services in the future. We are looking for someone who can balance rapid experimentation with operational rigor to build reliable services in a fast-moving research environment. Key job responsibilities - Be highly productive in the codebase and drive the team’s engineering velocity. - Design and evolve backend architecture and interfaces for core services. - Define and own standards for production health, performance, and observability. - Collaborate directly with Research, Human Feedback, Product Engineering, and other teams to understand workflows and define requirements. - Write technical RFCs to communicate design decisions and tradeoffs across teams. - Own projects end to end, from technical design through long-term maintenance. - Raise the team’s technical bar through thoughtful code reviews, architectural guidance, and mentorship.