Applied Scientist II
- Seattle, WA
How can we use Machine Learning (ML) to optimize our headcount planning process in call centers to optimally support our sellers, vendors, brands and associates? How can we leverage forecasting models to predict key planning inputs such as Average Handle Time, Incoming Seller /Vendor contacts, Associate Attrition etc.? How to optimize day-to-day staffing by leveraging intra-day data on multiple key planning inputs? How can we evaluate the causal impact of various targeted programs that aim to improve associate productivity and job satisfaction, seller retention etc.?
The answers to these questions and others like them are core to helping Amazon's Marketplace business thrive and expand, including delivering best-in-the-class seller, vendor and call center associate experience. Our cross-functional team works closely with various stakeholders world-wide such as Finance, Operations, Regional Capacity Planners, Global Planning, Hiring and Training to help them make data-driven business and hiring decisions.
Using AWS' large-scale computing resources the Applied Scientist (AS) will build ML models to optimize the headcount planning process. The AS will work with domain experts and engineers to help put those models into production and continuously improve their performance. The AS will interact with the Amazon ML community and mentor Scientists and Software Development Engineers with a strong interest in ML. Your work will directly benefit sellers, vendors, brands and associates on the Amazon Marketplace platform. We are looking for a passionate, hard-working, and talented AS who has experience building mission critical, big-data analytics solutions that deliver actionable insights.
Please visit https://www.amazon.science for more information
• MS in in Mathematics, Statistics, Computer Science (Machine Learning), Electrical Engineering, Physics or a related quantitative field.
• 3+ years of hands-on experience in predictive modeling.
• Knowledge of Time Series Modeling.
• Strong algorithm development experience.
• Proficient in scripting languages such as Python, Scala, Spark and R.
• Strong communication and data presentation skills.
The ideal candidate will have a PhD in Mathematics, Statistics, Computer Science (Machine Learning), Electrical Engineering, Physics or a related quantitative field, and 1+ years of relevant work experience, including:
• Experience applying theoretical models in an applied environment.
• Expertise in Time Series Modeling or Sequence Modeling.
• Experience on a broad set of ML approaches and techniques, ranging from Recursive Neural Network (e.g., LSTMs), Seq2Seq, ARIMA, GARCH, NLP.
• Knowledge of Probabilistic Modeling is an added plus.
• Expert in at least one scripting language such as Python, Scala, or R.
• Strong personal interest in learning, researching, and creating new technologies with high commercial impact.
• Significant peer reviewed scientific contributions in relevant field.
• Proven track in leading, mentoring and growing teams of scientists.
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
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