Research Scientist II, Demand Science

Key Skills

Java Programming, Analytical and Problem solving, Management, Python Programming, SQL, Agile methodologies, AWS, Scala Programming, Data science techniques, R Programming, Amazon RedShift, Amazon EMR, Linux Operating system, SPARK Programming, Data Analysis

Job Description

The Amazon Devices Demand Science team is looking for an energetic, focused and skilled, truly innovative and technically strong research scientist with a background in data analytics, machine learning, data science, decision science and statistical modeling/analysis to help with demand forecasting and planning for the entire Amazon device family of products, services and accessories.

Amazon is looking for a talented Research Scientist to join the Amazon Devices team. We materially impact Amazon’s device businesses by forecasting demand, influencing promotion pricing and identifying optimal inventory allocation of all Amazon Devices using ML, operations research and big data.

We are a growing team continues to operate in startup mode to prove new business ideas, while strengthening our core ML platforms.

Key job responsibilities

In this role, you will have an opportunity to both develop advanced scientific solutions and drive critical customer and business impacts. You will play a key role to drive end-to-end solutions from understanding our business requirements, exploring a large amount of historical data and ML models, building prototypes and exploring conceptually new solutions, to working with partner teams for prod deployment. You will collaborate closely with scientists, engineering peers as well as business stakeholders.

As a Research Scientist, you will:

• research and develop new methodologies for demand forecasting, alarms, alerts and automation with advanced models and methods.

• apply your advanced data analytics, machine learning skills to solve complex demand planning and allocation problems.

• work closely with stakeholders and translate data-driven findings into actionable insights.

• improve upon existing methodologies by adding new data sources and implementing model enhancements.

• create and track accuracy and performance metrics (both technical and business metrics).

• create, enhance, and maintain technical documentation, and present to other scientists, engineers and business leaders.

• drive best practices on the team; mentor and guide junior members to achieve their career growth potential.

A day in the life

This role will be a Problem Solver, Doer, Detail Oriented, Communicator and Influencer.

• Problem Solver: Ability to utilize exceptional modeling and problem-solving skills to work through different challenges in ambiguous situations.
• Doer: You’ve successfully delivered end-to-end operations research projects, working through conflicting viewpoints, model intractability, and data limitations.
• Detail Oriented: You have an enviable level of attention to details.
• Communicator: Ability to communicate analytical results to senior leaders, and peers.
• Influencer: Innovative scientist with the ability to identify opportunities and develop novel approaches in a fast-paced and changing environment, and gain support with data.

About the team

Our science team develops scalable and robust machine learning models and forecasting solutions that involve distributed data sources and ML models. The team is a young, focused science team looking for help with validating the technical decisions we are making and figuring out how to best solve our partner’s needs. This role is central to the continued growth of Amazon Devices division that includes Kindle E-Reader, Echo, Fire TV, Fire Tablet, E-Reader, Ring and many other devices. With better forecasts we drive down supply chain costs, enabling the offer of lower prices and better in-stock selection for our customers.

BASIC QUALIFICATIONS

• PhD or equivalent Master's degree plus 4+ years of research experience in a quantitative field
• Experience investigating the feasibility of applying scientific principles and concepts to business problems and products

• Proficiency in model development, model validation and model implementation.
• Strong programing skills in Python, R or Scala.

PREFERRED QUALIFICATIONS

• Experience with time series modeling and machine learning forecasting.
• Good knowledge of SQL, Spark, EMR, Redshift and AWS infrastructure.
• Experience with data management and/or high performance computing.
• Experience working in command-line Linux environments.
• Ph.D. in Data Science, Decision Sciences, Information Systems, Machine Learning, Operations Research, Industrial and Systems Engineering, Statistics, Applied Mathematics, Computer Science, Engineering, Business Analytics, or a related field
• Excellent quantitative modeling, data analysis, and problem-solving skills. Sophisticated user of machine learning, optimization and statistical tools.