DESCRIPTION Have you ever thought about what it takes to deliver high quality customer service at optimal cost? What would you do to take one of the best customer service organizations in the world to the next level? Do you enjoy working in an entrepreneurial, fast paced environment, solving complex problems and delivering innovative and high-impactful solutions? Do you like to innovate and simplify?
The Worldwide Capacity Planning (WWCP) organization owns the end-to-end workforce planning and execution of Amazon's customer service network. Our forecasting, labor planning, scheduling, and real-time management solutions are responsible for the millions of daily decisions needed to provide efficient and frustration-free support to our customers around the world. We are looking for an experienced Research scientist with strong research background, excellent technical skills and business acumen to join our Forecasting team.
As a part of the Forecasting team within the Worldwide Capacity Planning organization, you will partner closely with business leaders, Product Managers, Data Scientists, Economists, Research Scientists, Business Intelligence Engineers, Data Engineers and Software Engineers to build end-to-end forecasting solutions to deliver highly accurate customer contact demand forecasts, drive causal understanding of contact demand drivers from multimodality data, and extract actionable insights to excel business decision-making. As an research scientist, you will work on cutting edge research using a combination of econometrics, machine learning and statistics to exploit causal knowledge for customer contact drivers and shape decisions to deliver better customer service. You will bring statistical modeling and machine learning advancements to build state-of-the-art ML algorithms for forecasting. A successful candidate will be a problem solver and leading innovator, who is excited by difficult modeling challenges, and possesses strong communication skills to effectively interface between technical and business stakeholders.
Key job responsibilities
- Drive model development with a combination of econometrics, statistics, machine learning and analytical techniques to create scalable solutions for business problems.
- Collaborate with product, software engineering, research science, data engineering, and business leaders to build algorithms and models.
- Design, develop and evaluate of highly innovative models for forecast problems.
- Identify business problems, and formulate scientific solutions.
- Use the best practices in science: data integrity, design, test, and implementation and documentation.
- Mentor and guide junior members in the team.
- Contribute to Amazon's Intellectual Property through patents and internal and external publications.
A day in the life
"If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you're passionate about this role and want to make an impact on a global scale, please apply!"Benefits Summary:
"Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:
1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan"
We are open to hiring candidates to work out of one of the following locations:
Seattle, WA, USA BASIC QUALIFICATIONS - 3+ years of solving business problems through machine learning, data mining and statistical algorithms experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Strong technical skills and experience in Forecasting and Time series modeling or/and Causal Inference
