Senior Applied Scientist, CMO Science

The Campaign Measurement & Optimization (CMO) organization is looking for a Senior Applied Scientist interested in solving one of the most challenging business problems in marketing measurement and developing cutting-edge ML model.

Working with our team of data scientists, applied scientists, research scientists, and economists, this leader will help redefine scalable marketing measurement at Amazon and its subsidiaries.

The Campaign Measurement & Optimization (CMO) organization’s mission is to be the most trusted source of measurement science solutions to drive marketing investment decisions across Amazon.

The CMO team provides incrementality and efficiency measurement services to the marketing stakeholders across Amazon’s lines of business, including Stores, Prime Video, Amazon Devices, Alexa, Amazon Business, Amazon Music, Amazon Fresh, as well as subsidiaries including Audible, Ring, Whole Foods, and more.

CMO applies industry leading deep learning based causal inference models to measure omni-channel effectiveness of marketing campaigns from these businesses worldwide.

The impact and influence of the organization is tremendous, helping optimize spend decisions on a scale that exceeds many countries’ GDP.

Our outputs shape Amazon product and marketing teams’ decisions and therefore how Amazon customers see, use, and value their experience with Amazon.

This is a high-impact role with opportunities to develop systems and analyze marketing effectiveness that contributes billions of dollars to the business.

As a senior scientist, you will be responsible for leading the design and development of the cutting edge measurement and optimization models, while collaborating with businesses, marketers, and software teams to solve key challenges facing the teams.

Such challenges include measuring the incremental impact of multi-channel marketing portfolios, estimating the impact on sparse customer actions, and scaling measurement solutions for WW marketplaces.

Unlike many companies who buy existing off-the-shelf marketing measurement systems, we are responsible for studying, designing, and building systems to serve Amazon’s suite of businesses.

Our team members have an opportunity to be on the forefront of marketing measurement thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, economists and software developers in the business.

In this role, you will be a technical leader in applied science research with significant scope, impact, and high visibility.

You will lead strategic measurement science initiatives in CMO and across various marketing teams, scaling experimentation and development and deployment of the measurement science models, real-time inference, and cross-channel orchestration.

As a successful Scientist, you are an analytical problem solver who enjoys diving into data, leads problem solving, guides development of new frameworks, writes code, is excited about investigations and algorithms, and can credibly interface between technical teams and business stakeholders.

You are an expert in employing deep learning models to solve business problems, preferably in causal inference. You are a hands-on innovator who can contribute to advancing Marketing measurement technology in a B2C and B2B environment, and push the limits on what’s scientifically possible with a razor sharp focus on measurable customer and business impact.

You will also coach and guide scientists in the team to grow the team’s talent and scale the impact of your work.

We are open to hiring candidates to work out of one of the following locations :

Irvine, CA, USA Seattle, WA, USA Sunnyvale, CA, USA

BASIC QUALIFICATIONS

• 4+ years of applied research experience
• 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

PREFERRED QUALIFICATIONS

• 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.
30+ days ago