Job Description

Our Opportunity:

Chewy is looking for a Machine Learning Engineer II to join our Outbound Science Technology Team. In this role, you will combine an understanding of machine learning, simulation, data analysis, and software development. As a MLE, you will play a meaningful part in crafting, implementing, and deploying science models to address critical problems for our fulfillment Operations. The ideal candidate will operate as a full stack research scientist who should have expertise in both science models and cloud technologies, with expertise in deploying and scaling machine learning models in cloud environments. You will have the chance to create and develop backend ML/Optimization/Simulation frameworks best suited for different business problems and build engineering pipelines to streamline model deployment. Does this sound like you? If so, we would love to hear from you!

What you'll do:

• Design, develop, and implement machine learning models for various applications, including but not limited to resource planning optimization, predictive analytics, time-series forecasting and natural language processing
• Research and implement innovative science-based algorithms to address specific business challenges
• Design and implement end-to-end machine learning workflows (including data preprocessing, model training, and deployment) using AWS cloud (such as Sagemaker)
• Collaborate with multi-functional teams, including data scientists, software engineers, and domain experts, to understand requirements and deliver effective solutions
• Document code, algorithms and ensure reproducibility
• Provide technical mentorship in standard methodologies for model development and deployment to the data science team
• Effectively communicate technical concepts and insights to both technical and non-technical customers
• Deploy science models by using pipeline established by engineers using provisioning, cloud resource management and containerization as necessary

What you'll need:

• Candidate must possess a Graduate Degree (MS or PhD or equivalent experience) in Data Science, Machine Learning, Statistics, Operations Research, or related field
• 2+ years of experience in developing and deploying production-level systems by using combinations of algorithms (optimization and/or simulation) and machine learning models in a production environment
• Experience in the following areas: machine learning, forecasting, reinforcement learning, optimization and simulation. Understanding of deep learning techniques (Reinforcement Learning) is a plus
• Proficiency and expertise in developing science models using Python, Java or similar languages, as well as expertise in SQL
• Proficiency with version control systems (e.g., Git) and coding practices
• Strong understanding of cloud platforms for ML pipeline such as AWS Sagemaker
• Experience with containerization & orchestration tools (e.g. Docker, Kubernetes) and Infrastructure as Code tools (e.g., Terraform, CloudFormation) is a plus
• Strong problem-solving skills and the ability to work independently and in a fast-paced environment
• Excellent oral and written communication skills including collaboration with both technical and non-technical customers
• Ability to travel up to 10% of the time

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