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Vanessa Murdock is a classical pianist turned Amazon applied scientist

Vanessa Murdock, manager of Applied Science, talks about how her training as a classical pianist helps her be a better scientist, why she joined Amazon, and how her work at Amazon affects the lives of millions of customers.

Vanessa Murdock is a manager of Applied Science on the Amazon Alexa Shopping team. Vanessa is a trained classical pianist turned information retrieval researcher -- by no means your typical career journey. In this interview, Murdock talks about how her training as a classical pianist helps her be a better scientist, why she joined Amazon, and how her work at Amazon affects the lives of millions of customers.

Tell us a little about your background.

When I was younger, we lived with my grandparents for a few years. My grandfather was a labor lawyer. People often paid him in forms other than cash, and one of his clients paid him with a Steinway piano. I started by sounding out melodies on the piano, playing by ear. When I was four, my grandmother heard me piecing together a Mozart Symphony with both hands and a harmony, so she started to teach me. I didn’t take the lessons very seriously.

When I was 12, I broke my leg. I was bored because I had to stay inside a lot. As a result, I started practicing three or four hours a day. The difference between how well you play when you practice 20 minutes a day and when you practice four hours a day is vast. I started winning piano competitions, including one that sent me to Europe to give a concert at a music festival. Eventually, I received a scholarship at Texas Christian University (TCU), which hosts the Van Cliburn International Piano Competition. I went there to study with a Van Cliburn winner, Steven DeGroote – the Van Cliburn piano competition is held every four years; winners and runners-up receive cash prizes, in addition to the opportunity to perform at world-famous venues

What does it take to be a really good pianist?

You have to be very analytical and self-critical if you want to be a good pianist. You have to learn to hear how you sound as if you were sitting in the audience, and to be thoughtful about all the little choices you make. No detail is too small. Being analytical and self-critical have helped me a lot in computer science, at Amazon and in life in general.

How did you get into information retrieval?

When I started my career as a pianist, I took other work (I was a bookstore employee, I did housekeeping and food service, I worked in a dry cleaner) to supplement my income. As I became more established in the city I was living in, I was able to make a living solely from music jobs. I played in musical theaters, at weddings and parties, in churches, I taught privately and at a private school where I was also the staff accompanist, I performed as a soloist with orchestras and I had a trio that played concerts.

Although I was successful as a pianist, I was working 50 hours a week or more, and I was still struggling financially. When my son was born, it became clear that he would have fewer opportunities than I had, because I would not be able to give him a middle-class upbringing with extras like sports and music lessons. I was also a little burned out on teaching and accompanying. The part of music I loved was performing classical music, but I did not derive enough income from performing to do only that. I decided that I had to change paths.

I looked at a number of fields like journalism, political science and labor law. However, although they were interesting and would have been engaging, they also had long hours and low salaries. Then one day I was chatting with a friend on AOL messenger, and I started thinking about the magic of instant messaging: you can type a message and in an instant another person can read and respond, regardless of where they are in the world. I decided that I wanted to learn how computers work. My plan was to take a day job as a programmer, figuring it would provide a steady income with health insurance. It would only be 40 hours a week, which would leave me more time to focus on my performance career.

I enrolled at Colorado State University. To my great surprise, computer science was extremely fun, and much easier than piano. In the summer before my senior year, I took an internship at AT&T Research in New Jersey, working on machine translation with Srinivas Bangalore. The project was to mine the Web for parallel texts to train a machine translation system. A week into the internship I had an epiphany that computation was a tremendously powerful tool to understand fundamental questions about humanity, and I was hooked.

It was that internship, and Dr. Bangalore’s mentoring that showed me that instead of taking a “day job” testing printer drivers, I could do something really enriching. I was very fortunate that Dr. Bangalore encouraged open-ended exploration of the research questions. I had lofty goals at the time because I was inspired and idealistic, but I still find the big open questions about how people understand information to be the most compelling.

I decided that I wanted to do research, so I pursued a PhD. AT&T gave me a grant which included ongoing mentoring from Charles Thompson, who was on the board of the AT&T Fellowship program. Dr. Thompson helped me to understand that AT&T was supporting me because they saw in me a world class researcher. The combination of Dr. Bangalore’s big thinking and Dr. Thompson’s steady insistence that I could do significant science really changed the game for me. The lessons from the two of them infuse all of my work and all of my mentorship of new researchers.

Why did you join Amazon?

I am really excited about cloud-based voice services because voice will ultimately be a natural way for people to interact with their devices. Voice interfaces give us another picture of how people communicate. I like Amazon’s obsession for looking at problems from the customer perspective, and the potential to use science to directly improve the lives of millions of people.

The projects that I find most inspiring are the ones that allow me to understand customers better. My team is working on understanding what products are potentially embarrassing, and finding ways to be sensitive to these issues when providing experiences for our customers. For example, I don’t mind if people know I dye my hair because my hair is blue but another customer might be embarrassed if Alexa recommends dye with “full coverage for grey hair” in response to their shopping request.

I also love projects where we can help customers find what they are looking for or save them time. For example, people often reformulate their discovery query when they are not satisfied with their results. They might start by querying for “latte,” before reformulating their query to “espresso machines” to get more relevant results. My team’s research allows us to build experiences that help our customers find what they are looking for faster.

What’s different about working at Amazon?

One thing that’s really different at Amazon is how we discuss ideas and plans as a document that everyone reads through together. This seemed like overkill the first time I saw it, but a couple weeks in, I realized that a six-page narrative is a great equalizer. When ideas are presented verbally, they can be less convincing if the presenters are not skilled, or unduly credible if the presenters are charismatic and able to charm the audience into supporting a weak idea. Further, the audience may think they agree with a proposal, but actually misunderstand it, leading to serious friction down the line. Having the information presented as a document resolves much of this because the document is concrete and it can be edited to be clearer, and referred back to when there are questions later. If all the stakeholders agree on the substance of the document, it becomes their contract. It is the most effective way I have seen to come to an understanding as a group and make a rigorous group decision.

Amazon is optimized for shipping innovations quickly - the amount of time to go from first idea to customer-facing product is much shorter than at other places I have worked. People show genuine excitement and energy for what they are doing, and what they could do in the future. Everyone is completely focused on making a meaningful difference for customers. As a result, many good decisions are baked into our mechanisms, rather than being the result of an afterthought.

As scientists, our best ideas come from a deep understanding of a problem. You can have a certain depth of understanding by reading papers and running simulations, but it does not compare to the depth of understanding you gain from making scientific advances on real systems that are useful and relevant to people. The change from music to computer science was a huge change, but being at the front of a technological revolution is exciting and I am honored to play a part.


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Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, CA, Irvine
Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, WA, Seattle
Job summaryAre you excited about using econometrics to make multi-million dollar decisions more Science and Data Driven? Are you interested in supporting Consumer Hardware device concepts from innovative idea inception to launch? Do you want to work on a Economics and Data Science team focused on tackling some of the hardest business questions within the Devices business at Amazon and then scaling those Statistics and Econometrics solutions via internal to Amazon tools? Then this could be the role for you!Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Amazon Devices and Services team is the area of Amazon focused on inventing platforms that delight customers by eliminating friction they have in supplying, entertaining, and managing the home and beyond.The Device Economics team owns demand estimates and pricing recommendations of concept devices before customers know they exist. We support over 100 device-specific analyses a year on hardware and services ranging from Echo Frames to Kindle Paperwhite to Blink Video Camera subscriptions to the Amazon Smart Plug…all prior to launch.. We are a cross-functional Product team working to scale Econometrics through Amazon and beyond by incorporating Science into internal facing tools and making it easier for others to do so as well.In this role, you will support up to senior leadership decision meetings around approving confidential funding requests (PRFAQs) for brand new devices and services, build decisions around how many hardware devices to manufacture prior to receiving any customer signal, and pricing decisions around how to price and promote products and services. You will leverage Science and Tools produced by the Device Economics team such as conjoint demand models to produce these recommendations. As part of the stakeholder-facing arm of the team, you will own relationships with decision makers to help improve the end-customer experience by making the decisions that impact those end-customers more data and Science-driven. In parallel, you will work with Scientists, Economists, Product Managers, and Software Developers to provide meaningful feedback about stakeholder problems to inform business solutions and increase the velocity, quality, and scope behind our recommendations. You will own projects to make progress on Decision Science itself. Through this all, we will invest in your development to pursue your career goals.We are willing to consider L5 candidates across the BA/BIE job families where we'll bar raise your Science skills.
US, WA, Seattle
Job summaryAlexa Smart Home Research is looking for a brilliant quantitative researcher to drive a new program to ensure Alexa always delivers a four star experience. In this role, you will define the roadmap for the SH segmentation program, create experiments to evaluate customer behavior and sentiment that drive these higher quality experiences for our target customers. These insights help Alexa Smart Home Marketing and Product teams make data driven decisions about our marketing and product strategies ensuring products are accurately conveyed, appropriately priced and designed, and with each launch we are moving the needle for customers to help them accomplish their ideal smart home.Key job responsibilities· Identify and propose key opportunities for improving the product development and marketing strategy for Alexa products· Develop and execute research projects, including leading all project phases: methodology and study design, data gathering and manipulation, analysis, interpretation and presentation of results· Lead and execute validation and impact studies· Define project requirements, document business and functional specifications, map current and future state business processes· Build automated mechanisms for evaluating, measuring, and deploying the algorithms and/or models you develop.· Bring a deep level of expertise in one of the Research Marketing disciplines (e.g. Statistics)A day in the lifeAs part of your work, you will lead quantitative research projects that build our understand of smart home customers, identifying what works well and areas of improvement for Alexa Smart Home that will ensure we continue to delight our customers. Excellent business and communication skills are a must to develop and define key business questions and to build data sets that answer those questions. You should be able to work with business customers in understanding the business requirements and research impact.About the teamWe are responsible for UX and market research (foundational, market fit, usability and concept testing), Beta launch readiness and voice of the customer. These services product org-wide customer insights that help SH teams connect directly with customers daily, supporting the end-to-end product readiness, and look around the corner to understand customer and competitor trends.
US, CA, Irvine
Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, WA, Seattle
Job summaryAre you excited about cutting-edge deep-learning NLP, NLU, and Conversational AI? If so, then come and join the Alexa Artificial Intelligence (AI) team. We are the science team behind Amazon’s intelligence voice assistance system and are responsible for the deep learning technology that is central to the automated ranking and arbitration to optimize for end-to-end customer satisfaction.Key job responsibilitiesAs an Applied Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.A day in the life· Design, build, test and release predictive ML models· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, and transformation.· Collaborate with colleagues from science, engineering and business backgrounds.· Present proposals and results to partner teams in a clear manner backed by data and coupled with actionable conclusions· Work with engineers to develop efficient data querying and inference infrastructure for both offline and online use casesAbout the teamWe are a science and engineering team part of Alexa AI organization. Our mission is to help Alexa decide which action to take in response to customer requests, incorporating a variety of contextual signals including both direct and indirect customer feedback to provide the best response to the customer. Our work directly contributes to improvement in Alexa business and customer metrics.