- Masters in Computer Science, Mathematics, Engineering, or related quantitative discipline, or a BS with 4+ years of experience.
- Deep understanding of regression modeling, forecasting techniques, time series analysis, machine-learning concepts such as supervised and unsupervised learning, classification, random forest, etc.
- Experience in developing machine-learning algorithms, statistical and mathematical optimization models, and simulation and visualization tools
- Familiarity with managing disparate data sets; including, building and maintaining data flows and pipelines
- Experience with SQL and at least one scripting language (preferable Python)
- Exposure to big data: extraction, processing, filtering, and presenting large data quantities (100K to Millions of rows) via AWS technologies, SQL, and data pipelines
- Industry experience in defining and building metrics, performing business analysis, and quantifying decisions through the utilization of data
- Ability to communicate technical concepts and solutions at a level appropriate for technical and non-technical audiences
- Advanced ability to draw insights from data and clearly communicate them to the stakeholders and senior management as required.
AWS has the most services and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. AWS Platform is the glue that holds the AWS ecosystem together. Whether its Identity features such as access management and sign on, cryptography, console, builder & developer tools, and even projects like automating all of our contractual billing systems, AWS Platform is always innovating with the customer in mind. The AWS Platform team sustains over 750 million transactions per second.
Are you passionate about building robust services that serve millions of customers with high availability? Do you want to use data to identify risks? Do you want to make the lives of builders using AWS better by reducing the number of failures in their software services? If so, you might be looking for an exciting career in AWS Safety Engineering as a data scientist! Our team is looking for a passionate, solution-oriented, full-stack Data Scientist to help design and deliver on a strategy to analyze risk data and use it to drive business decisions and program development with stakeholders.
As a Data Scientist, you will build analytical solutions to synthesize information, provide project direction, and communicate business and technical requirements within the team and across stakeholder groups. You are indispensable. You consider the needs of day-to-day operations and insist on the standards required to build the network of tomorrow. Your job will straddle day-to-day decisions and strategic vision, you will assist in defining trade-offs and quantifying opportunities for a variety of projects. You will learn current processes, educate diverse stakeholder groups, work with engineers to design initial solutions and requirements, and audit all model development and implementation. A successful candidate in this position will have a background in communicating across significant differences, prioritizing competing requests, and quantifying decisions made.
This position requires superior analytic thinkers, who are able to quickly approach large ambiguous problems and apply their technical and scientific knowledge to identify opportunities for further research. The ideal candidate will demonstrate creativity in problem solving and enjoy owning projects end-to-end. You should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so.
Learn and Be Curious. We have a formal mentor search application that lets you find a mentor that works best for you based on location, job family, job level etc. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members.
Inclusion and Diversity. Our team is diverse! We drive towards an inclusive culture and work environment. We are intentional about attracting, developing, and retaining amazing talent from diverse backgrounds. Team members are active in Amazon’s 10+ affinity groups, sometimes known as employee resource groups, which bring employees together across businesses and locations around the world. These range from groups such as the Black Employee Network, Latinos at Amazon, Indigenous at Amazon, Families at Amazon, Amazon Women and Engineering, LGBTQ+, Warriors at Amazon (Military), Amazon People With Disabilities, and more.
Learn more about Amazon on our Day 1 Blog: https://blog.aboutamazon.com/
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
- Experience with processing, analyzing, and modeling with large amount of data
- Fluency in a scripting or computing language (e.g. Python, R, Java, C++, etc.)
- Experience with agile/scrum methodologies and its benefits of keeping scientists on track and iteratively delivering results.
- PhD in Computer Science, Mathematics, Engineering, or related quantitative discipline.
Data Science Masters Program makes you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau. The curriculum has been determined by extensive research on 5000+ job descriptions across the globe.
What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!
What is data science and how can you use it to strengthen your organization? This course will teach you about the skills you need on your data team, and how you can structure that team to meet your organization’s needs. Data is everywhere! This course will provide you with an understanding of data sources your company can use and how to store that data. You’ll also discover ways to analyze and visualize your data through dashboards and A/B tests. To wrap up the course, we’ll discuss exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI! Along the way, you’ll learn about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.
The book begins by establishing the concept of cloud computing and describing the technological trends, and then discusses cloud computing architecture connotation and key technologies such as computing, storage, network, data, management, access, and security. With abundant project experiences and applications, the book is an essential reference for researchers and industrial engineers in computer science and information management.ed manner
The continuing importance of data analytics is not lost on higher education leaders, who face a multitude of challenges, including increasing operating costs, dwindling state support, limits to tuition increases, and increased competition from the for-profit sector. To navigate these challenges, savvy leaders must leverage data to make sound decisions. In Big Data on Campus, leading data analytics experts and higher ed leaders show the role that analytics can play in the better administration of colleges and universities.
The authors of Big Data with Hadoop MapReduce: A Classroom Approach have framed the book to facilitate understanding big data and MapReduce by visualizing the basic terminologies and concepts. They employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines.
Is it possible to take advantage of the benefits of data collection–and mitigate risks–for both companies and customers?
Most consumers are not very skilled at evaluating privacy risks; they’re either unable to determine the cost of sharing personal data online or unaware of what they’re sharing. (Doesn’t everyone scroll down without reading to click “I accept”?) Without much intervention from most federal or state-level governments, companies are on their own to define what qualifies as reasonable use. In today’s digital surveillance economy, there are no clear-cut best practices or guidelines. Gathering and using information can help customers–we see that in personalization and autofill of online forms. But companies must act in the best interest of their customers and treat the sensitive information users give them with the ethical care of doctors, lawyers, and financial advisers. The challenges of operating in a digital ecosystem aren’t going away. Customer Data and Privacy: The Insights You Need from Harvard Business Review will help you understand the tangled interdependencies and complexities and develop strategies that allow your company to be good stewards, collecting, using, and storing customer data responsibly.