Applied Scientist, Advertising Incrementality Measurement
Amazon Advertising is looking for a motivated and analytical self-starter to help pave the way for the next generation of insights and advertising products.
You will use large-scale data, advertising effectiveness knowledge and business information needs of our advertising clients to envision new advertising measurement products and tools. You will facilitate innovation on behalf of our customers through end-to-end delivery of measurement solutions leveraging experiments, machine learning and causal inference. You will partner with our engineering teams to develop and scale successful solutions to production.
This role requires strong hands-on skills in terms of effectively working with data, coding, and MLOps. However, the ideal candidate will also bring strong interpersonal and communication skills to engage with cross-functional partners, as well as to stay connected to insights needs of account teams and advertisers.
This is a truly exciting and versatile position in that it allows you to apply and develop your hands-on data modeling and coding skills, to work with other scientists on research in new measurement solutions while at the same time partner with cross-functional stakeholders to deliver product impact.
Key job responsibilities
As an Applied Scientist on the Advertising Incrementality Measurement team you will:
- Create new analytical products from conception to prototyping and scaling the product end-to-end through to production.
- Scope and define new business problems in the realm of advertising effectiveness. Use machine learning and experiments to develop effective and scalable solutions.
- Partner closely with the Engineering team.
- Partner with Economists, Data Scientists, and other Applied Scientists to conduct research on advertising effectiveness using machine learning and causal inference. Make findings available via white papers.
- Act as a liaison to product teams to help productize new measurement solutions.
About the team
Advertising Incrementality Measurement combines experiments with econometric analysis and machine learning to provide rigorous causal measurement of advertising effectiveness to internal and external customers.
We are open to hiring candidates to work out of one of the following locations:
Arlington, VA, USA | Boulder, CO, USA | New York, NY, USA | Santa Monica, CA, USA
- 3+ years of building models for business application 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
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in professional software development
- Experience with popular deep learning frameworks such as MxNet and Tensor Flow
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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/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.