Machine Learning Engineer II

Chewy is looking for a Machine Learning Engineer II with experience in recommendations and search relevance to join our Discovery team in Seattle or Boston. 

Millions of pet parents with unique needs visit Chewy.com looking for products for their beloved pets!  We have the task of deciding what products would be most useful to them and help them discover those products. How do we do this? 
 Meet Discovery team @ Chewy. We use the best machine learning techniques and continuously test the outcomes to simplify product discovery for pet parents looking for their pet needs on Chewy.com. Our exceptional multi-disciplinary team of data scientists, data engineers, software engineers and product managers work together to power personalized recommendations and product discovery for pet parents. Our team has single threaded ownership of the space allowing us to decide impactful products that we can experiment with, measure with metrics and deliver at a fast pace. 

What You'll Do: 

• You will deploy machine learning and data-science to simplify shopping experience for pet-parents, to improve reach and discovery of new products of Chewy vendors and helping create a great outcome for everyone ecosystem.
• Be responsible for the full Data Science lifecycle from conception to prototyping, testing, deploying, and measuring the overall business value of the models.
• Conduct exploratory analysis on large sets of text data and make recommendations to identify customer problems and inform new product features.
• Devise models and algorithms and guide engineering to develop scalable solutions that can work in real-time with large amounts of data.
• Identify and run priorities within the context of overall Chewy objectives, and work with colleagues in data science and engineering to drive the standard processes in development, testing, and production of models at scale.
• Establish strong working relationship at all organizational levels across different functional teams.
• Work with the data engineering teams to develop the automated pipelines to perform different stages of the model life cycle.
   

What You'll Need: 

• An advanced degree (M.S., PhD, or equivalent experience) in Statistics, Applied Mathematics, Data Science or related field or proven experience for at least 3 years designing optimization and machine learning solutions for large scale applications.
• Experience in building and shipping machine learning models for business applications at scale.
• Experience with deep learning software platforms, such as PyTorch or TensorFlow.
• Experience with groundbreaking technologies in deep learning, RecSys, transformer architectures, two-tower architectures and Search, Ranking or large-scale embeddings.
• Demonstrated knowledge and experience with distributed machine learning and deploying models at scale in cloud environments such as AWS or Google Cloud Platform.
• Excellent verbal and written communication skills. Able to explain details of complex concepts to non-expert Team Members in a simple understandable way.
• You enjoy working on a team of highly engaged individuals, have a passion for data science and important metrics, and want to work on the cutting edge of your field.
• Proven experience in seeing opportunities for business improvement and defining and measuring the success of those initiatives.