Garrett van Ryzin
Garrett van Ryzin joined Amazon's Supply Chain Optimization Technologies organization in August as a distinguished scientist.
Credit: Jesse Winter/Cornell University

How distinguished scientist Garrett van Ryzin is optimizing his time at Amazon

van Ryzin is focusing on driving innovations in areas ranging from inventory management to last-mile delivery.

Amazon announced in August 2020 that Garrett van Ryzin would be joining the company’s Supply Chain Optimization Technologies (SCOT) organization as a distinguished scientist. SCOT is responsible for designing, building, and operating the Amazon supply chain. SCOT systems manage inventory for the millions of items on Amazon, compute accurate delivery expectations for customer orders, and drive meaningful changes to Amazon’s fulfillment center network so that customers receive their packages in the most efficient way possible.

Prior to Amazon, van Ryzin was a professor of Operations, Technology and Information Management at Cornell Tech, and previously the Paul M. Montrone Professor of Decision, Risk, and Operations at the Columbia University Graduate School of Business.  His university research work has focused on algorithmic pricing, demand modeling, and stochastic optimization.

van Ryzin was also the head of marketplace optimization at ridesharing companies Lyft and Uber, where he led teams that developed models for a variety of functions, such as optimally dispatching drivers to riders, and developing pricing models and driver pay systems that improve market efficiency. Interestingly, van Ryzin’s paper that he wrote while pursuing his PhD at MIT “A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane” imagined a world of on-demand transportation as far back as 1991.

During his career, van Ryzin’s work on complex revenue management problems has enabled businesses across diverse industry sectors to get the most out of their limited capacity. To give just a few examples, van Ryzin’s research has enabled airlines to make a series of large-scale, dynamic and sequential decisions to determine the optimal price of a ticket at a particular moment in time. Retail companies have used similar dynamic optimization to manage inventory levels and prices for different products to maximize revenue.

What I find particularly interesting are problems that move beyond the constraints of optimizing within the system, to actually redesigning the system itself. 
Garrett van Ryzin

However, at Uber and Lyft van Ryzin tackled a new business environment, where revenue maximization wasn’t the primary goal. Instead, van Ryzin’s teams focused on optimizing more immediate metrics that were vital to the very survival of their services: service reliability, driver productivity, and growth.

For example, having a sufficient number of idle drivers at any given time is critical to maintaining throughput in ridesharing services. Surge pricing, a mechanism that van Ryzin’s team at Uber optimized, maintains an efficient level of idle drivers and encourages more drivers to get on the street during peak hours when they are needed the most.

van Ryzin sees technology-enabled service providers — be it at a ridesharing company like Lyft or the Fulfilled by Amazon (FBA) service — as transformational.  Only a few decades ago, businesses like these weren’t viable ways to organize service delivery due to high transaction costs and lack of real-time information. However, technology has radically improved information exchange and reduced transaction costs, which allows independent sellers to sell their products on Amazon much more efficiently than they could on their own.

In this interview, van Ryzin spoke about the different facets of market optimization, the intricacies of making automated decisions at scale, managing system complexity using approximation and decomposition ideas, and why he joined Amazon.

Q. What are the different elements of optimization?

I’d like to think of optimization being made up of human, technical and operational elements.

At a human level, the understanding of behavioral economics is absolutely critical. You have to create the right incentives for both suppliers and buyers to drive efficiencies. This is especially important for companies like Amazon that have many buyers and sellers participating and a high degree of decentralized activity. 

In addition to the human considerations, you also must develop a deep understanding of the technical elements of how these marketplaces work – the capabilities and limitation of the technology – which in turn allows you to gain insights into what structural changes are possible.

Finally, building services like Amazon that provide physical goods and services is a much more complicated endeavor than developing a service for trading virtual entities like stocks or mutual funds. To give just one example, at Amazon we are shipping actual, physical goods. This means the underlying physics of the infrastructure and the different operational elements are critical. So you must also think about your service in terms of factors like product weight and size, labor requirements, storage capacity, inventory levels, and lead times.

From a scientific perspective, there are several open questions in all three elements of market optimization. A fundamental one is determining the best approach to take to develop models to drive efficiency.

One approach is to develop structural models from first principles. For example, you could make an assumption that consumers are utility maximizers, develop a utility function and identify the parameters that constitute this utility function.

Garrett van Ryzin
Garrett van Ryzin, Amazon distinguished scientist

You could also take a radically different approach and build models based only on the underlying data – where you draw inferences from what the data alone tells you. Here, you’re not worrying about why something happened. Rather, you can use ideas from machine learning to estimate and refine predictive models without trying to understand the underlying mechanics.

What I find particularly interesting are problems that move beyond the constraints of optimizing within the system, to actually redesigning the system itself.  The ‘Wait and Save’ feature my group developed at Lyft is a good example. This product allows riders to opt into waiting for ten to fifteen minutes for a ride rather than having all rides be on-demand. In exchange for waiting, riders get a lower price. On the technology side, what we are doing here is actually changing the product in order to make the marketplace more efficient. I’ve always found there’s a lot more leverage in changing a system rather than optimizing within a fixed system.  It’s a lot trickier though because big structural changes often mean you have to get users comfortable with entirely new products or a completely new way of using the system.

Q. How do you account for the uncertainty and complexity inherent in large systems?

Approximation is at the heart of optimization because you can never fully represent the full complexity of a real-world trading system. For example, if a consumer places an order on Amazon, you have to make several sequential decisions with complex interactions.  Which fulfillment center should I take that order from? Should I place the items in the same box or should I pack them in different boxes? How will fulfilling this order impact the availability of inventory for the next order that comes in for that product? And how will it affect the available capacity of my local delivery assets?

You can develop approximation models by using a rolling horizon approach. This involves taking a best guess for what the future entails, and then updating your estimate for the future as and when you get new information. Or you could do something that’s far more sophisticated: build simulations of the future, and use sampling techniques to guide your decisions. You can also utilize reinforcement learning where you fit value functions to historical actions to arrive at decisions that are continually refined based on data.

Decomposition is also an important strategy for dealing with the interconnectedness of the different elements of the system. In large systems such as Amazon, everything is related to everything else. Supply affects costs, which affects pricing, which in turn affects demand, which affects dispatch, and so on. Ideally, you’d want to arrive at decisions by taking the whole system into account. However, the size of any real-world system makes this impossible. Any model you arrive at will be too complex, and you’d require a large amount of time to compute anything reasonable.

I’ve always been attracted to the idea of helping drive innovations to get people the basic, physical necessities that are essential to how they live.
Garrett van Ryzin

This is where decomposition comes in. You can break the system down into individual components – such as dispatch models, pricing models, inventory models and so on. The challenge here is to get these different models to collaborate. You don’t want scenarios where they are working at cross purposes with each other. For example, you don’t want one model trying to get rid of an item and have another model actively trying to replace it. In cases like these, you can drive coordination between different models using an internal price or some other mechanism that’s common to all the models.

These are just some of the trickiest issues in optimization, and I’m excited to be at Amazon where a lot of the innovation in these areas is taking place.

Q. Why did you decide to join Amazon?

I’ve always admired Amazon as a company because of its incredible track record of innovation across so many areas. I remember shopping at Amazon when they just sold books. And today, you have Amazon Studios, AWS, Amazon Devices, Alexa and even Project Kuiper where Amazon is putting up over 3,000 satellites in space.

Amazon is a company that excels at understanding economic opportunity and then building products and services that customers value. I’ve only been here for a few months, but I can already see how the company’s unique culture helps it be so successful across so many areas.

I also admire the company’s long-term perspective. Amazon doesn’t make decisions based on driving quarter-over-quarter performance. Amazon is willing to stick with ideas for many years. This appeals to me as a scientist as in my experience, sticking with the right idea over the long term is essential to making fundamental breakthroughs.

At SCOT, I’m excited to have the opportunity to contribute across so many areas, from FBA to last-mile delivery. Over the last few months, Amazon has helped so many people across the world get essential items during the pandemic. I’ve always been attracted to the idea of helping drive innovations to get people the basic, physical necessities that are essential to how they live.

Related content

GB, London
"Are you a MS or PhD student interested in the fields of Computer Science or Operational Research? Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products? If this describes you, come join our research teams at Amazon. " Key job responsibilities The candidate will be responsible for the design and implementation of optimization algorithms. The candidate will translate high-level business problems into mathematical ones. Then, they will design and implement optimization algorithms to solve them. The candidate will be responsible also for the analysis and design of KPIs and input data quality. About the team ATS stands for Amazon Transportation Service, we are the middle-mile planners: we carry the packages from the warehouses to the cities in a limited amount of time to enable the “Amazon experience”. As the core research team, we grow with ATS business to support decision making in an increasingly complex ecosystem of a data-driven supply chain and e-commerce giant. We take pride in our algorithmic solutions: We schedule more than 1 million trucks with Amazon shipments annually; our algorithms are key to reducing CO2 emissions, protecting sites from being overwhelmed during peak days, and ensuring a smile on Amazon’s customer lips. We do not shy away from responsibility. Our mathematical algorithms provide confidence in leadership to invest in programs of several hundreds millions euros every year. Above all, we are having fun solving real-world problems, in real-world speed, while failing & learning along the way. We employ the most sophisticated tools: We use modular algorithmic designs in the domain of combinatorial optimization, solving complicated generalizations of core OR problems with the right level of decomposition, employing parallelization and approximation algorithms. We use deep learning, bandits, and reinforcement learning to put data into the loop of decision making. We like to learn new techniques to surprise business stakeholders by making possible what they cannot anticipate. For this reason, we work closely with Amazon scholars and experts from Academic institutions. We are open to hiring candidates to work out of one of the following locations: London, GBR
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a highly-skilled Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and push the boundaries of efficient inference for Generative Artificial Intelligence (GenAI) models. As a Senior Applied Scientist, you will play a critical role in driving the development of GenAI technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Design and execute experiments to evaluate the performance of different decoding algorithms and models, and iterate quickly to improve results - Develop deep learning models for compression, system optimization, and inference - Collaborate with cross-functional teams of engineers and scientists to identify and solve complex problems in GenAI - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | New York, NY, USA
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, and basic familiarity with Python or R, is necessary. Experience with SQL is a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and apply econometric methods to support business decisions, collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Collaborate with business and science colleagues to understand the business problem and collect relevant data. Provide statistically rigorous analysis of data that contributes to business decision-making. Effectively communicate your results to colleagues and business leaders. A day in the life Meet with colleagues to discuss how the business currently works. Discuss ways in which the customer experience could be improved, and what data you'd need to test your hypotheses. Meet with data and business intelligence engineers to build an efficient data pipeline using SQL, spark and other big data tools. Propose and execute a plan to analyze your data, working closely with your econ colleagues. Use Amazon's development tools, coding your estimators in Python or R. Draft your findings for an internal knowledge sharing session. Iterate to improve your work and communicate your final results in a business document. About the team We are a team of four economists that works within the delivery experience org. Our goal is to improve the delivery experience for our customers while reducing costs. This mission puts us in a unique position to influence both the front end customer experience and the supply chain that ultimately places constraints on that experience. This means we often work with and influence teams outside of our own organization. As a result, we have the privilege of working with a diverse group of experts, including those in supply chain, operations, capacity management, and user experience. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
ES, B, Barcelona
"Are you a MS or PhD student interested in the fields of Computer Science or Operational Research? Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products? If this describes you, come join our research teams at Amazon. " Key job responsibilities The candidate will be responsible for the design and implementation of optimization algorithms. The candidate will translate high-level business problems into mathematical ones. Then, they will design and implement optimization algorithms to solve them. The candidate will be responsible also for the analysis and design of KPIs and input data quality. About the team ATS stands for Amazon Transportation Service, we are the middle-mile planners: we carry the packages from the warehouses to the cities in a limited amount of time to enable the “Amazon experience”. As the core research team, we grow with ATS business to support decision making in an increasingly complex ecosystem of a data-driven supply chain and e-commerce giant. We take pride in our algorithmic solutions: We schedule more than 1 million trucks with Amazon shipments annually; our algorithms are key to reducing CO2 emissions, protecting sites from being overwhelmed during peak days, and ensuring a smile on Amazon’s customer lips. We do not shy away from responsibility. Our mathematical algorithms provide confidence in leadership to invest in programs of several hundreds millions euros every year. Above all, we are having fun solving real-world problems, in real-world speed, while failing & learning along the way. We employ the most sophisticated tools: We use modular algorithmic designs in the domain of combinatorial optimization, solving complicated generalizations of core OR problems with the right level of decomposition, employing parallelization and approximation algorithms. We use deep learning, bandits, and reinforcement learning to put data into the loop of decision making. We like to learn new techniques to surprise business stakeholders by making possible what they cannot anticipate. For this reason, we work closely with Amazon scholars and experts from Academic institutions. We are open to hiring candidates to work out of one of the following locations: Barcelona, B, ESP
IN, TN, Chennai
DESCRIPTION The Digital Acceleration (DA) team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms for solving Digital businesses problems. Key job responsibilities - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues BASIC QUALIFICATIONS - Experience building machine learning models or developing algorithms for business application - PhD, or a Master's degree and experience in CS, CE, ML or related field - Knowledge of programming languages such as C/C++, Python, Java or Perl - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. PREFERRED QUALIFICATIONS - 3+ years of building machine learning models or developing algorithms for business application experience - Have publications at top-tier peer-reviewed conferences or journals - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment We are open to hiring candidates to work out of one of the following locations: Chennai, TN, IND
US, VA, Arlington
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: - Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries - Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them - Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Atlanta, GA, USA | Austin, TX, USA | Houston, TX, USA | New York, NJ, USA | New York, NY, USA | San Francisco, CA, USA | Santa Clara, CA, USA | Seattle, WA, USA
US, WA, Seattle
Prime Video offers customers a vast collection of movies, series, and sports—all available to watch on hundreds of compatible devices. U.S. Prime members can also subscribe to 100+ channels including Max, discovery+, Paramount+ with SHOWTIME, BET+, MGM+, ViX+, PBS KIDS, NBA League Pass, MLB.TV, and STARZ with no extra apps to download, and no cable required. Prime Video is just one of the savings, convenience, and entertainment benefits included in a Prime membership. More than 200 million Prime members in 25 countries around the world enjoy access to Amazon’s enormous selection, exceptional value, and fast delivery. 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! Key job responsibilities As a Data Scientist at Amazon Prime Video, you will work with massive customer datasets, provide guidance to product teams on metrics of success, and influence feature launch decisions through statistical analysis of the outcomes of A/B experiments. You will develop machine learning models to facilitate understanding of customer's streaming behavior and build predictive models to inform personalization and ranking systems. You will work closely other scientists, economists and engineers to research new ways to improve operational efficiency of deployed models and metrics. A successful candidate will have a strong proven expertise in statistical modeling, machine learning, and experiment design, along with a solid practical understanding of strength and weakness of various scientific approaches. They have excellent communication skills, and can effectively communicate complex technical concepts with a range of technical and non-technical audience. They will be agile and capable of adapting to a fast-paced environment. They have an excellent track-record on delivering impactful projects, simplifying their approaches where necessary. A successful data scientist will own end-to-end team goals, operates with autonomy and strive to meet key deliverables in a timely manner, and with high quality. About the team Prime Video discovery science is a central team which defines customer and business success metrics, models, heuristics and econometric frameworks. The team develops, owns and operates a suite of data science and machine learning models that feed into online systems that are responsible for personalization and search relevance. The team is responsible for Prime Video’s experimentation practice and continuously innovates and upskills teams across the organization on science best practices. The team values diversity, collaboration and learning, and is excited to welcome a new member whose passion and creativity will help the team continue innovating and enhancing customer experience. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, NJ, Newark
Employer: Audible, Inc. Title: Data Scientist II Location: 1 Washington Street, Newark, NJ, 07102 Duties: Design and implement scalable and reliable approaches 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 solution 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. 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. 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, 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 into Newark, NJ office; however, telecommuting from a home office may be allowed. Requirements: Requires a Master’s in Statistics, Computer Science, Data Science, Machine Learning, Applied Math, Operations Research, Economics, or a related field plus two (2) years of Data Scientist or other occupation/position/job title with research or work experience related to 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: - Building statistical models and machine learning models using large datasets from multiple resources - Non-linear models including Neural Nets or Deep Learning, and Gradient Boosting - Applying specialized modelling software including Python, R, SAS, MATLAB, or Stata. One (1) year in the following: - Using database technologies including SQL or ETL. Alternatively, will accept a Bachelor's and five (5) years of experience. Multiple positions. Apply online: www.amazon.jobs Job Code: ADBL135. We are open to hiring candidates to work out of one of the following locations: Newark, NJ, USA
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of audio technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in AGI in audio domain. About the team Our team has a mission to push the envelope of AGI in audio domain, in order to provide the best-possible experience for our customers. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA
DE, BE, Berlin
Are you fascinated by revolutionizing Alexa user experience with LLM? The Artificial General Intelligence (AGI) team is looking for an Applied Scientist with background in Large Language Model, Natural Language Process, Machine/Deep learning. You will be at the heart of the Alexa LLM transition working with a team of applied and research scientists to bring classic Alexa features and beyond into LLM empowered Alexa. You will interact in a cross-functional capacity with science, product and engineering leaders. Key job responsibilities * Work on core LLM technologies (supervised fine tuning, prompt optimization, etc.) to enable Alexa use cases * Research and develop novel metrics and algorithms for LLM evaluation * Communicating effectively with leadership team as well as with colleagues from science, engineering and business backgrounds. We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU