Amazon Principal Data Scientist Workforce Intelligence 

United States, Texas, Arlington 

2083172

Key job responsibilities
You will be the science advisor to the head of WFI, driving overall strategy for the WFI science team. You will influence strategic decisions around which domains WFI should invest science resources in, and how to augment the science capabilities on the team through development and hiring.
On a day-to-day basis you will be responsible for making sure that the scientists on the team are solving the right problems, using the right techniques for the problem at hand, drawing the right conclusions and communicating appropriately to both the tech and non-tech stakeholders. You will lead a science panel facilitating this oversight of the data scientists, research scientists and economists on the WFI science team. Every science model goes through science panel review before getting deployed. You will raise the standards of science development and delivery. You will improve the review mechanism to deliver high quality outcomes while minimizing overhead.
You will take the lead in solving the most challenging science problems on the roadmap. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).

- MS in Mathematics, Statistics, Machine Learning, or a related quantitative field
- 10+ years industry experience working on data science and machine learning projects
- Deep understanding and practical experience in several of the following areas: machine learning, statistics, optimization and forecasting
- Demonstrated problem-solving skills with the ability to apply algorithms, which may include data profiling, clustering, anomaly detection, and predictive modeling methodologies

- PhD in Mathematics, Statistics, Machine Learning, or a related quantitative field
- 12+ years of experience working in data science
- Significant peer reviewed scientific contributions in Data Science, Analytics, Statistics, Optimization or related field.
- Extensive experience applying theoretical models in an applied environment
- Strong fundamentals in problem solving and algorithm design
- Ability to uphold a high bar for science development across multiple domains