Our Opportunity:
Chewy is focused about our customers experience. We move fast to rethink and redesign our supply chain optimization frameworks from ground up. If the opportunity to build a best-in-class supply chain network, make a large business impact, and deliver large scale savings intrigue you, we will want to talk to you!
Chewy is seeking a Research Scientist II of a balanced background in optimization, machine learning (ML), and simulation to join the Supply Chain Outbound Science team. You will be instrumental in driving the transformation towards ML-based replenishment models. The goal is to ensure adaptability to constant changes and complexity in our supply chain systems. This includes finding, constructing and deploying the right models from the ground up to optimize our outbound fulfillment for customer orders. Chewy’s Supply Chain team operates in a fast-paced environment where every day brings new challenges and new opportunities.
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What You’ll Do:
- Develop and implement models and solutions to solve complex supply chain problems using optimization, machine learning, and relevant simulation or artificial intelligence (AI) techniques.
- Deep dive large supply chain data to explain change, develop insights, and provide recommendations.
- Work with stakeholders across organizations (with Inbound, S&OP, Transportation, and Supply Planning) to understand business requirements and translate them into technical specifications for models and algorithms.
- Implement process improvement initiatives that drive improvements to the metrics and streamline the Supply Chain.
- Communicate technical concepts and results to non-technical stakeholders in a clear and concise manner.
What You’ll Need:
- An advanced degree (M.S., PhD (preferred), or equivalent experience) in Data Science, Operations Research, Applied Mathematics, or related fields.
- Strong background in Optimization and machine learning with practical experience in developing and deploying models in cloud environments like AWS, Azure, Google Cloud, etc.
- Two years working experience on solving supply chain business problems is highly preferred.
- Prior experience working with deep learning models like NNs, CNNs, LSTM, etc. and RL models, highly preferred.
- Proficiency in Python coding including ML libraries
- Proficiency in solving optimization problems using Gurobi, Xpress, or CPLEX
- Ability to use collaborative programming tools (Git, Confluence, etc.)
- Ability to translate complex data sets and research into simple business recommendations.
- Ability to manage multiple projects and prioritize competing requirements.
Compensation & Benefits:
Our salary range for a Research Scientist II position is $125,000.00 - $199,500.00. The specific salary offered to a candidate may be influenced by a variety of factors including but not limited to the candidate’s relevant experience, education, and work location. In addition, this position is eligible for 401k, and a new hire and annual equity grant.
We offer different types of insurance, such as medical/Rx, vision, dental, life, disability, hospital indemnity, critical illness, and accident. We offer parental leave, family services benefits, backup dependent care, flexible spending accounts, telemedicine, pet adoption reimbursement, employee assistance program, and many discounts including 10% off pet insurance and 20% off at Chewy.com.
Salaried-exempt team members have unlimited PTO, subject to manager approval. Team members will receive six paid holidays per year. Team members may be eligible for paid sick and family leave in compliance with applicable state and local regulations.
Chewy is committed to equal opportunity. We value and embrace diversity and inclusion of all Team Members. If you have a disability under the Americans with Disabilities Act or similar law, and you need an accommodation during the application process or to perform these job requirements, or if you need a religious accommodation, please contact CAAR@chewy.com.
If you have a question regarding your application, please contact HR@chewy.com.
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