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Description

Amazon’s Automated Marketing and Experiences (AME) team is building the Internet's largest-scale Search and Social Marketing systems. We are looking for an inquisitive, life long learner with a keen interest on evidence driven decision making, e-commerce and understanding of customer behavior with digital marketing. We are seeking a researcher with interest in e-commerce industry and relevant experience to leverage and help evolve our offsite marketing experimentation frameworks. Our candidate will have strong causal inference, experimental design, decision theory and bayesian statistics background. This is a role to flesh out a nascent new program that aspires to shape industry trends. So this is genuinely a unique opportunity for candidates to emerge as world class experts in a fast evolving, ever green domain.

Our mission is to engage customers with the right products and services to enable a great shopping experience. You will go home and show your family and friends why they receive this ad on your search engine or via social media applications from Amazon. You will make a difference by improving the relevancy for customers and optimizing the investment level for Amazon. State-of-the-art technology and algorithms including econometric methods, statistical modeling, machine learning, and data mining are the core of our business. Marketing drives a large portion of Amazon’s traffic and business, and represents a unique opportunity to drive impact on the company’s bottom line. We also focus on developing novel A/B experimentation mechanisms to measure efficacy of our ML solutions. With essentially full ownership of our own product roadmap, there is a large R&D component to our work with together with sound business understanding and an appetite for innovation are highly valued.

Key job responsibilities

- Design and build scalable analytic solutions using statistical models to measure the business impact of cross channel marketing treatments.

- Work closely with both business units and engineering teams to formulate business problems in experimental designs and associated technical solution strategies.

- Develop a library of measurement solutions to enable data-driven decision making.

- Provide inputs to the product roadmap, emphasizing research and development (R&D) to continuously enhance bidding capabilities.

- Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.

A day in the life

As a Senior Research Scientist on our team, you will leverage you strong background in statistics and causal inference to help build the next generation of offsite marketing experimentation frameworks. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. The ideal candidate will have experience with causal inference, experimental design, decision theory and bayesian statistics. We are particularly interested in experience in building large scale marketing experimentation frameworks.

About the team

We are a team of scientists who are set to build solutions in production. We work on prediction, optimization, and experimentation problems to provide data-driven inputs to marketing decisions and build highly scalable statistical frameworks and machine learning models across Automated Marketing and Events (AME) org to drive long-term profitability. Specifically, the team focuses on building re-usable science solutions to address three focal areas: (i) Content selection, creation and moderation, (ii) Bidding which involves valuation, efficiency management and net-profit maximization via elasticity measurement, and (iii) Scalable Experimentation frameworks and statistical techniques for designing and performing causal analysis. efficiency management and net-profit maximization via elasticity measurement, and (iii) Scalable Experimentation frameworks and statistical techniques for designing and performing causal analysis.

Basic Qualifications

- PhD, or Master's degree and 6+ years of investigating the feasibility of applying scientific principles and concepts to business problems and products experience

- Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field

- Knowledge of R or Python

- Advanced proficiency with statistical modeling, experimental design, and machine learning algorithms.

- Ability to research, identify, evaluate, and implement modeling solutions for complex business problems.

- Communicating scientific research and trade-offs for long- and short-term objectives to stakeholders from senior business leaders to highly technical engineering teams.

Preferred Qualifications

- Experience converting research studies into tangible real-world changes

- Experience with discrete and continuous optimization methodologies and algorithms

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $143,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.