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Description

The Campaign Measurement & Optimization (CMO) organization is looking for a Applied Science Manager 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 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 team lead, you will be responsible for developing / coaching the talent, guiding the team on 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, research 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 measurement science models, real-time inference, and cross-channel orchestration. As a successful team lead, you are an analytical problem solver who enjoys diving into data, leads problem solving, guides development of new frameworks, 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 coach and guide scientists to grow the team’s talent and scale the impact of your work.

Basic Qualifications

• 3+ years of scientists or machine learning engineers management experience

• Knowledge of ML, NLP, Information Retrieval and Analytics

• Expert in developing large-scale ML systems in a production environment

• Extensive experience applying theoretical models in an applied environment

• Demonstrated proficiency in deep learning models, experience building production level causal inference models

• Expert in more than one more major programming / scripting languages (Python, Scala, PySpark or similar)

• Excellent written and verbal communication skills while addressing both technical and business people; ability to speak at a level appropriate for the audience.

• Experience coaching and reviewing work of junior ML Scientists, making great hiring decisions.

Preferred Qualifications

• Experience building machine learning models or developing algorithms for business application

• Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers

• Team building and science recruiting experience

• Comprehension of tech stacks and could stay on top of tactical execution

• Strong doc writing skills

• Strong fundamentals in problem solving, algorithm design and complexity analysis

• Proven track record of delivering ML models in production

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 $165,500/year in our lowest geographic market up to $286,000/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.