Image shows Cristiana Lara, a research scientist, standing outside and smiling with a tree in the background
Cristiana Lara, a research scientist, has done groundbreaking work on network timing that is helping Amazon better formulate how to transport packages more efficiently.

Cristiana Lara's journey from a curious student to an Amazon research scientist

Today she's helping Amazon to better formulate how to more efficiently transport packages through the middle mile of its complex delivery network.

When Cristiana Lara was growing up in Rio de Janeiro, she became obsessed with challenging math problems, working on them feverishly until she could solve them. Now she sees a direct link between a love for puzzling inspired by her technically oriented parents — her father was an electrical engineer and her mother a STEM teacher — and her current work as an Amazon research scientist.

Lara joined Amazon in 2019, after completing her doctorate in process systems engineering at Carnegie Mellon University’s Center for Advanced Process Decision Making. That was the same year Amazon unveiled free, one-day delivery for select Amazon Prime customers in the US. Thus her start date meant Lara began work on a most daunting mission: the development and implementation of an optimization framework to support the company’s transportation network.

While the company has focused on making its delivery systems as efficient and cost-effective as possible, Amazon’s growth, scale, and drive to meet customer demand put a considerable strain on its delivery network. Optimization is a constantly moving target that requires long-term strategic planning, and that is where Lara centers her attention. The focus of her research: develop models and algorithms for solving large-scale discrete optimization problems.

My models and tools actually get to change business decisions and have a direct, positive impact for our customers.
Cristiana Lara

Lara has already made an impact in her short time at Amazon. Her groundbreaking work on network timing, through a planning tool appropriately named ‘TICTOC,’ is helping Amazon to better formulate how to more efficiently transport packages through the “middle mile” of its complex delivery network.

“I particularly like the fact that at Amazon, the work that I do is core to the business,” she said. “My success is not measured by how many papers I publish, but it’s about how my models and tools actually get to change business decisions and have a direct, positive impact for our customers.”

TICTOC is an acronym for Transportation Intraday Capacity planning for Timing Optimization Computation. Lara developed key advancements within it to support timing-related decisions in the transportation network design space.

Used for long-term planning, TICTOC provides the ability to perform sensitivity analysis and understand how different variables in the network design impact the overall delivery speed. With its international network of fulfillment centers, sort centers, and delivery stations, Amazon has built a complex, real-time delivery organization that relies heavily on coordinated timing and an ability to make in-the-moment adjustments in order to fulfill its ambitious customer-delivery promises, and to meet the company’s goal of having 50 percent of all shipments net zero carbon by 2030.

The goal is to understand the tradeoff between transportation costs and delivery speed, and then make more informed decisions regarding the big picture questions that the company will face in the near and distant future. Determining optimal package flow over time in order to maximize one-day delivery while minimizing cost requires a dizzying array of algorithms. When do you schedule the trucks to depart?  How many are needed? And when and where are they needed at any given time?

“Those problems are hard to solve because of their discrete nature,” Lara said, “and there’s a lot of theory behind it. They get a lot harder when the problem gets bigger because of the combinatorial explosion.” Solving these problems on a smaller, regional basis is already feasible. But deciphering them for large, continental geographies, such as the entire US, is where the task gets tougher.

For Lara, translating these issues into action items that can have a dramatic impact on the company’s success is “something that I like a lot,” she said. The harder the problem, the more jazzed she is to address it.

After graduating from the Federal University of Rio de Janeiro with a degree in chemical engineering, Lara realized quickly that it was not the discipline that fit her long-range ambitions. Her advisor in Brazil suggested she look into process systems engineering, replete with its modeling and optimization skillset, and she was hooked. As she began her PhD program at Carnegie Mellon, she moved farther away from chemical engineering, and steered more in the direction of operations research. There, the interface between applied math and coding offered a chance to see her work impact decision making in operations, manufacturing, logistics, and a variety of business applications.

Having eschewed offers from academia for a tenure track position, Lara transformed an internship at Amazon into a full-time research job.

“I interned in the same team I currently work for, and my project was to develop a tool combining stochastic simulation and machine learning to forecast the package flow between origin-destination pairs in the Amazon network,” Lara recalled. “The network design optimization models need to consume these kinds of forecasts to be able to plan for the connectivity — how to connect the nodes in the network and how much package flow to expect between nodes and within each node.”

Lara said internships are also a good way for students to figure out their path forward. “My advice is this: Students should take advantage of the opportunity and do as many and as diverse internships as they can. It’s a great way to get to know themselves, what motivates them, the type of working culture that matches their personality, and what they want for their career.”

Amazon hosted more than 10,000 interns virtually this summer. If you’re a student with interest in an Amazon internship, you can learn more about internship opportunities at Amazon Student Programs.

Having gone that route herself, today Lara finds herself doing work that has real-world impact. To that end, she was nominated by her bosses for an invitation to the prestigious U.S. Frontiers of Engineering symposium, sponsored by the National Academy of Engineering, which was held in Sept. 22 - 24 in Irvine, Calif. Lara presented a poster at the symposium which brought together 83 of the nation’s outstanding young engineers from industry, academia, and government in a variety of disciplines to discuss pioneering technical issues and leading-edge research in various engineering fields.

Dr. Gregory D. Abowd, the dean of Northeastern University’s College of Engineering, was a 2002 participant in the conference. “The purpose is to seed conversations on important global and national problems with a number of smart and open-minded individuals,” Abowd said. “You can say you want to have an impact in the world, but to do so, you have to step out of your discipline and be comfortable thinking on a larger scale.”

The symposium put him in the same room as a group of future leaders in their fields which left him feeling “empowered and emboldened.” For Lara, “It certainly is a vote of confidence from her employer that she has the right kind of expertise and broad-minded, potential leadership capabilities that are worth nurturing,” he added.

“For me, it’s a great opportunity to be among other early career engineers in different fields and be able to talk about my research and their research and learn from them,” Lara said. “Amazon has a lot of researchers and they know that to keep researchers happy, we need to be able to talk about our research, because that’s what excites us.”

View from space of a connected network around planet Earth representing the Internet of Things.
Sign up for our newsletter


US, WA, Seattle
Job summaryWW Installments is one of the fastest growing businesses within Amazon and we are looking for an Economist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits Amazon.com. We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way. Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.Economists at Amazon are solving some of the most challenging applied economics questions in the tech sector. Amazon economists apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. Our economists build econometric models using our world class data systems, and apply economic theory to solve business problems in a fast-moving environment. A career at Amazon affords economists the opportunity to work with data of unparalleled quality, apply rigorous applied econometric approaches, and work with some of the most talented applied econometricians in the trade.As the Economist within WW Installments, you will be responsible for building long-term causal inference models and experiments. These analysis represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how objective functions are designed and which inputs are consumed for modeling. You will work across functions including machine learning, business intelligence, data engineering, software development, and finance to induce data driven decisions at every level of the organization.Key job responsibilitiesThis role will be responsible for:• Developing a causal inference and experimentation roadmap for the WW Installments Competitive Pricing team.• Apply expertise in causal and econometric modeling to develop large-scale systems that are deployed across Amazon businesses.• Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.• Lead the project plan from a scientific perspective on product launches including identifying potential risks, key milestones, and paths to mitigate risks• Own key inputs to reports consumed by VPs and Directors across Amazon.• Identifying new opportunities to influence business strategy and product vision using causal inference.• Continually improve the WW Installments experimentation roadmap automating and simplifying whenever possible.• Coordinate support across engineers, scientists, and stakeholders to deliver analytical projects and build proof of concept applications.• Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.
US, WA, Seattle
Job summaryWW Installments is one of the fastest growing businesses within Amazon and we are looking for an Applied Scientist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits Amazon.com. We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way. Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.As an Applied Scientist within WW Installments, you will be responsible for building machine learning models and pipelines with direct customer impact. These models represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how they interact with financing options to make purchases. You will work across functions including data engineering, software development, and business to induce data driven decisions at every level of the organization.Key job responsibilitiesThis role will be responsible for:• Developing production machine learning models and pipelines for the WW Installments Competitive Pricing team that directly impact customers.• Apply expertise in machine learning to develop large-scale production systems that are deployed across Amazon businesses.• Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.• Lead the implementation of production ML from a scientific perspective including identifying potential risks, key milestones, and paths to mitigate risks.• Identifying new opportunities to influence business strategy and product vision using data science and machine learning.• Continually improve the WW Installments ML roadmap automating and simplifying whenever possible.• Coordinate support across engineers, scientists, and stakeholders to deliver ML pipelines, analytics projects, and build proof of concept applications.• Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.
US, WA, Seattle
Job summaryWW Installments is one of the fastest growing businesses within Amazon and we are looking for a Data Scientist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits Amazon.com. We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way. Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.As a Data Scientist within WW Installments, you will be responsible for building machine learning models and pipelines with direct customer impact. These models represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how they interact with financing options to make purchases. You will work across functions including data engineering, software development, and business to induce data driven decisions at every level of the organization.Key job responsibilitiesThis role will be responsible for:• Developing machine learning models and pipelines for the WW Installments Competitive Pricing team.• Apply expertise in machine learning to develop large-scale systems that are deployed across Amazon businesses.• Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.• Lead the project plan from a scientific perspective on product launches including identifying potential risks, key milestones, and paths to mitigate risks.• Own key inputs to reports consumed by VPs and Directors across Amazon.• Identifying new opportunities to influence business strategy and product vision using data science and machine learning.• Continually improve the WW Installments ML roadmap automating and simplifying whenever possible.• Coordinate support across engineers, scientists, and stakeholders to deliver ML pipelines, analytics projects, and build proof of concept applications.• Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.
US, CA, San Diego
Job summaryPrivate Brands is fast-growing within Amazon, and is a highly visible, emerging business. We have a unique business and obsess over quality and building global brands our customers love. We aspire to be part of our customers’ everyday lives by offering them unique products at compelling prices backed by Amazon’s strong customer obsessed reputation.Private Brands Intelligence (PBI) is looking for a Data Scientist to join our team in building Machine Learning solutions at scale. PBI applies Machine Learning, Causal Inference, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon’s retail business. We also develop statistical models and algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Economists, Scientists, and Engineers incubating and building Day One solutions using cutting-edge technology, to solve some of the toughest business problems at Amazon.You will work with business leaders, scientists, economists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. You will partner with scientists, economists, and engineers to help invent and implement scalable ML and econometric models while building tools to help our customers gain and apply insights.This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Consumer economy, and work closely with scientists and economists. We are particularly interested in candidates with experience building predictive models and working with distributed systems.As a Data Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.
US, VA, Arlington
Job summaryThis role will sit in our new headquarters in Northern Virginia, where Amazon will invest $2.5 billion dollars, occupy 4 million square feet of energy efficient office space, and create at least 25,000 new full-time jobs.The AWS Infrastructure Data Center Planning and Delivery (DCPD) Data Science team owns supply chain management activities at a global scale.We consolidate usage and supply chain health data and forecasts at a variety of horizons to ensure that we have the right strategic lens associated with each decision we make.We identify gaps to ensure that the AWS business is able to support any and all customers who want to capitalize on the scalability, flexibility, and cost-efficiency of AWS. Our actions and decisions decide the where, how, and what will make it into each of our data centers and we need you to help us to make those decisions and clearly explain the why.The Business Insights and Optimization (BIO) team owns data science, engineering, and business intelligence solutions feeding this team.We identify gaps in our capacity planning and delivery mechanisms and design/build systems which will fix those gaps.We are end to end data product owners and the analysis, models we produce drives billions of dollars of decisions annually.Data Scientists on this team have end to end range and capabilities.They work directly with business owners to understand how they use data to drive their business.They design modeling frameworks to dive deep into these raw sources of information to get the most out of the data they have.They work directly with data engineers to build automated pipelines and production scale information systems and models.They build automated tools which will allow their results to be shared with the business at scale.They align with business owners to continuously track their work to ensure maximum impact from their projects.They monitor performance of their work to evaluate whether improvements are needed after tracking has started in production.
US, CA, Sunnyvale
Job summaryAmong the goals of the Alexa Devices AI team, is to make Alexa the most knowledgeable and trusted ally for notifications, annoucements, pickup services and voice assistance while on the go.Key job responsibilities1. As an Applied Scientist on our team you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art NLU (Natural language understanding) developments.2. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to traing Machine Learning models for their application in NLU.3. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations.4. The ideal candidate will have experience with machine learning models and their application in AI systems. We are particularly interested in experience applying natural language processing, deep learning at scale. Additionally, we are seeking candidates with strong interest in data/research sciences and engineering, creativity, curiosity, and great judgment.5. You will interact with various stake holders: product leaders, program managers, other domain managers and developers on regular basis for requirement collections, deliveries, and other related communication6. You will help attract and recruit technical talentA day in the lifeApplied Scientist will help develop novel algorithms and apply modeling techniques to advance the state of the art in spoken language understanding (SLU) and to improve the customer experience in engaging with Alexa.About the teamThe Alexa Devices AI science team's work directly impacts the experience and engagement of customers who rely on Alexa while in-the-car, on-the-go and at-home.
US, VA, Arlington
Job summaryThe Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.We are looking for an economist with expertise in applying causal inference, experimental design, or causal machine learning techniques to topics in labor, personnel, education, health, public, or behavioral economics. We are particularly interested in candidates with experience applying these skills to strategic problems with significant business and/or social policy impact.Candidates will work with economists and engineers to estimate and validate their models on large scale data, and will help business partners turn the results of their analysis into policies, programs, and actions that have a major impact on Amazon’s business and its workforce. We are looking for creative thinkers who can combine a strong economic toolbox with a desire to learn from others, and who know how to execute and deliver on big ideas.Ideal candidates will own key inputs to all stages of research projects, including model development, survey administration, experimental design, and data analysis. They will be customer-centric, working closely with business partners to define key research questions, communicate scientific approaches and findings, listen to and incorporate partner feedback, and deliver successful solutions.
US, CA, Palo Alto
Job summaryAmazon is the 4th most popular site in the US (http://www.alexa.com/topsites/countries/US). Our product search engine is one of the most heavily used services in the world, indexes billions of products, and serves hundreds of millions of customers world-wide. We are working on a new AI-first initiative to re-architect and reinvent the way we do search through the use of extremely large scale next-generation deep learning techniques. Our goal is to make step function improvements in the use of advanced Machine Learning (ML) on very large scale datasets, specifically through the use of aggressive systems engineering and hardware accelerators. This is a rare opportunity to develop cutting edge ML solutions and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include:· Can a focus on compilers and custom hardware help us accelerate model training and reduce hardware costs?· Can combining supervised multi-task training with unsupervised training help us to improve model accuracy?· Can we transfer our knowledge of the customer to every language and every locale ? The Search Science team is looking for a Senior Applied Science Manager to drive roadmap on making large business impact through application of Deep Learning models via close collaboration with partner teams. The team also has a focus on technology solution for deep-learning based embedding generation, sensitive data ingestion and applications, data quality measurement, improvement, data bias identification and reduction to achieve model fairness.Success in this role will require the courage to chart a new course. You will manage your own team to understand all aspects of the customer journey. You and your team will inform other scientists and engineers by providing insights and building models to help improving training data quality and reducing bias. The research focus includes but not limited to Natural Language Processing, recommendation, applications relevant to Amazon buyers, sellers and more. You will be working with cutting edge technologies that enable big data and parallelizable algorithms. You will play an active role in translating business and functional requirements into concrete deliverables and working closely with software development teams to put solutions into production.
US, WA, Seattle
Job summaryAmazon EC2 provides cloud computing which forms the foundation for the majority of AWS services, as well as a large portion of compute use cases for businesses and individuals around the world. A critical factor in the continued success of EC2 is the ability to provide reliable and cost effective computing. The EC2 Fleet Health and Lifecycle (EC2 FHL) organization is responsible for ensuring that the global EC2 server fleet continues to raise the bar for reliability, security, and efficiency. We are looking for seasoned engineering leaders with passion for technology and an entrepreneurial mindset. At Amazon, it is all about working hard, having fun and making history. If you are ready to make history, we want to hear from you!Come join a brand new team, EC2 Health Analytics, under EC2 Foundational Technology, to solve complex cutting-edge problems to power a faster, more robust and performant EC2 of tomorrow. The charter of our team is to improve customer experience on the EC2 fleet by analyzing hundreds of signals and driving next-generation detection and remediation tools. We apply Machine Learning to predict outcomes and optimize decisions that improve customer experience and operational efficiency. As an Applied Scientist in the EC2 Health Analytics team, you will join an industry-leading engineering team solving challenging problems at massive scale.· Build a world-class forecasting platform that scales to handling billions of time series data in real time.· Drive fleet utilization improvement where each 1% means tens of millions of additional free cash flow.· Automate tactical and strategic capacity planning tools to optimize for service availability and infrastructure cost.· Build recommendation algorithms for improving the AWS customer experience.· · Reduce dependence on manual troubleshooting for deep-dives.What you will learn:· State-of-the-art analytics and forecasting methodologies.· Application of machine learning to large-scale data sets.· · Product recommendation algorithms.· Resource management and admission control for the Cloud.· The internals of all AWS services.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, CA, Palo Alto
Job summaryThe 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, the 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 team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide.