The Customer Behavior Analytics team designs innovative machine learning solutions to enhance customer experiences and strengthen their relationship with Amazon. This interdisciplinary team of scientists and engineers incubates and develops disruptive solutions using state-of-the-art technology to tackle some of the most challenging scientific problems in customer behavior analysis at Amazon. To achieve this, the team utilizes methods from Natural Language Processing, deep learning, large language models (LLMs), affinity models, elasticity models, reinforcement learning, and econometrics to drive personalized experiences throughout the customer journey. Our solutions are crucial to improving customer satisfaction and loyalty, serving as a model for customer-centric solutions across the company.

This role presents a high-visibility opportunity for someone eager to make a business impact, delve into large-scale problems, drive measurable actions, and collaborate closely with scientists and engineers. As a team lead, you will be responsible for developing and coaching talent, guiding the team in designing and developing state-of-the-art, and working with business, marketing, and engineering teams to address key challenges. These challenges include building and improving incentive recommendation models, developing Neural Networks or LLM-based recommendation systems, and creating models to enhance the relationship between customers and Amazon.

In this role, you will be a technical leader in applied science research with substantial scope, impact, and visibility. A successful team lead will be an analytical problem solver who enjoys exploring data, leading problem-solving efforts, guiding the development of new frameworks, and engaging in investigations and algorithm development. You should be capable of effectively interfacing between technical teams and business stakeholders, pushing the boundaries of what is scientifically possible, and maintaining a sharp focus on measurable customer satisfaction and business impact.

Key responsibilities include:

1. Developing and deploying sophisticated incentive recommendation models to optimize customer engagement and loyalty.
2. Leading the creation of advanced Neural Network or Large Language Model-based recommendation systems to provide personalized experiences.
3. Designing and implementing models that improve the overall relationship between customers and Amazon, focusing on long-term customer value and satisfaction.
4. Applying reinforcement learning methods in production to continuously improve and adapt customer behavior models.
5. Guiding the team in leveraging state-of-the-art AI/ML techniques to extract insights from vast amounts of customer data.

Additionally, you will mentor and guide scientists to enhance the team's talent and expand the impact of your work. Your role will be crucial in shaping the future of customer behavior analytics at Amazon, driving innovation that directly impacts millions of customers worldwide.

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
- 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
- External Publications

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 $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.