Senior Applied Scientist, Digital Advertising

We are seeking a talented Applied Scientist to join our team and help us build innovative systems at the intersection of machine learning and quantitative marketing. We develop new measurement and optimization tools that enable Amazon to make smart marketing investment decisions. This role will impact billions of dollars of decision-making by Amazon’s most strategic businesses. Our most effective tools are released to external advertisers too, defining new industry standards. What makes us unique is our comprehensive data, our world-class engineering systems and a high concentration of some of the most talented scientists and engineers in industry.

As a successful candidate, you will be passionate about building scalable systems. You'll be comfortable with ambiguity and have exceptional technical acumen. You'll be up to speed on the latest research, and capable of developing new techniques at the intersection of causal inference, reinforcement learning and quantitative marketing. You will lead other scientists by example, with crisp technical writing and frequent presentations.

Key job responsibilities
- Build end-to-end causal machine learning solutions.
- Perform hands-on analysis and modeling with enormous data sets to better understand how advertising influences shopper behavior.
- Run A/B experiments that affect millions of customers to evaluate the impact of your solutions.
- Spearhead new research agendas and own delivering on commitments.
- Work closely with engineering to design systems that facilitate high velocity science exploration and quick prototype-to-prod timelines.
- Develop novel methods at the intersection of causal inference, machine learning and quantitative marketing.
- Present original research at internal and external conferences.

About the team
Our team is a dynamic mix of scientists who are passionate about innovating. We are excited to evaluate our novel causal models and optimization algorithms against ground truth generated by large-scale experiments. We think big, take risks and stay grounded. - 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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.

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 $150,400/year in our lowest geographic market up to $260,000/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.