AWS Data Scientist

Key Skills

Java Programming, Python Programming, SQL, AWS, R Programming

Job Description

Job summary

Amazon Web Services’ Marketing Science team is looking for an applied scientist with strong technical skills in machine learning and forecasting to drive methodology improvements and tool inventions for marketing planning, goal tracking, targeting and segmentation, while developing our long term roadmap.

The successful candidate will be a hands-on self-starter, comfortable with ambiguity, humble to seek feedback and learn from peers (scientists, economists, marketing practitioners), bold to think big, has solid attention to detail and believes in challenging themselves to raise the bar continuously. The scientist will develop production models using geographical, macro-economic and customer-level panel data on forecasting, and account or segment based marketing. They will work closely with media and portfolio teams to understand business needs and with an engineering team to productionize solutions.

This role requires an individual with strong quantitative modeling skills and the ability to apply statistical/machine learning, econometric, and coding skills.

The candidate should have strong communication skills to work closely with stakeholders to translate business needs into methodology and data-driven findings into actionable insights. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and ability to work in a fast-paced and ever-changing environment.

Key job responsibilities

• Simplify and drive automation of the customer targeting process by building new tools and onboarding existing ones from Amazon Retail or AWS
• Design and implement machine learning models in partnership with business stakeholders and other scientists on the team
• Work with the data acquisition team on data requirements
• Work closely with both business units and engineering teams to formulate forecasting and targeting problems and associated technical solution strategies
• Work with peer science and marketing teams in Amazon or AWS to identify technical solutions that could be used or adopted in AWS Marketing forecasting and targeting framework
• Develop a library of forecasting and machine learning algorithms to enable data-driven decision making
• Support engineering teams to build tools and applications on our unique big data platform to efficiently generate and deploy insights into decision-making systems at AWS
• Raise the bar on applications of machine learning and deep learning for forecasting and targeting

About the team

Inclusive Team Culture

Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Work/Life Balance

Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth

Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.

BASIC QUALIFICATIONS

• PhD or equivalent Master's Degree plus 4+ years of experience in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Operations Research, Economics or related field
• 2+ years of experience using Python, R or other traditional programming language is required
• 2+ years of experience in SQL

PREFERRED QUALIFICATIONS

• 2+ years of experience building statistical and machine learning models for forecasting and targeting
• 1+ years of experience researching, identifying, evaluating, and implementing modeling solutions for complex business problems
• PhD in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Operations Research, Economics or Related Fields
• Proficient in software design principles and application on large-scale ML models
• Experience working with engineering to productionize ML models
• Experience presenting data-focused research to upper management and non-technical stakeholders