- Bachelor’s Degree
- 5+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- 4+ years working as a Data Scientist
- PhD in Education, Public Policy, Statistics, Marketing, Psychology, Sociology or a related field
- 5+ years conducting end-to-end scientific research in an applied setting (e.g., exploratory data analysis, generating testable hypotheses, developing measurement tools, conducting causal inference, demonstrating impact to stakeholders)
- Demonstrable expertise on research design methodologies for experiments, and quasi-experimental studies
- Proficient with SQL and at least one scripting language (e.g., R, Python)
- Excellent written and verbal communication skills for both technical and non-technical audiences
Are you passionate about conducting research to drive changes and improve the employee experience of a million Amazonians globally? The Organizational Research and Measurement (ORM) team is hiring a Senior Data Scientist to help us lead research initiatives to measure and improve Amazon’s organizational culture as it relates to health and safety of our employees. The role will drive behavioral and organizational changes, support data-driven decision making by business leaders, and facilitate development of innovative products that improve the safety outcomes and overall employee experience in the World Wide Consumer organization.
The candidate will join a diverse team of social scientists, statisticians and computer scientists who are working on science initiatives to optimize the employee experience across the full employee lifecycle, from first contact through exit, through the use of technology and cutting edge social science research.
The ideal candidate should have a strong business acumen as well as a broad technical skillset and flexible analytical approach. This role will need to navigate complex and ambiguous business challenges by asking the right questions, understanding what methodologies to employ, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders).
Major responsibilities will include:
- Conduct experimental and quasi-experimental studies to measure the impact of various initiatives and policies on Amazon’s safety culture and safety outcomes.
- Query data from multiple sources, perform data cleaning and exploration, and drive advanced statistical analysis.
- Develop high-quality, evidence-based documents that provide insights to business leaders and gain stakeholder buy-in.
- Serve as a subject matter expert on topics related to research design, measurement, and analysis.
- Collaborate with other ORM and Amazon scientists with expertise in areas such as machine learning, econometrics, psychometrics, natural language processing, computer vision, forecasting, and optimization.
- Familiarity with Time Series Analysis and/or Predictive Modeling techniques. Experience will Bayesian methods.
- Prior well-established recognized academic experience, multiple publications in top-tier academic journals
- Experience with qualitative research methodologies such as ethnography, and focus groups
- Familiar with machine learning tools and data infrastructure in Amazon Web Service
- Ability to engage business partners and stakeholders and solve an ambiguous business problems with appropriate choice of data science solutions.
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.
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.