Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions.
There are numerous scientific and technical challenges you will get to tackle in this role, such as optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning.
As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.
You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques.
- Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions.
- Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams.
- Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution.
- Develop offline policy estimation tools and integrate with measurement systems/econometric models.
- Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation.
- Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes.
- Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.
- Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.
- Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
Key job responsibilities
- Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions.
- Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams.
- Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution.
- Develop offline policy estimation tools and integrate with measurement systems/econometric models.
- Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation.
- Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes.
- Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.
- Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.
- Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
BASIC QUALIFICATIONS
- 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.
