Job Description

Our Opportunity:

Do you want to build AI solutions that directly impact the company’s bottom line? Do you want to see how your models, pipelines, and prototypes shape decision-making at the highest level? If so, the Chewy Marketing Science team is hiring! We use advanced AI, machine learning, statistics to understand and serve the needs of our pet parents. As part of the Marketing Science team, you will solve a wide range of business problems for marketing and Chewy customers—spanning predictive modeling, causal inference, and next-generation AI applications. Our team has single-threaded ownership of building data science and AI products powered by ML/Stats models, agentic AI workflows. In this role, you will have the opportunity to rapidly prototype GenAI and agentic AI solutions, proactively identify opportunities for innovation, and partner closely with marketing stakeholders to embed these tools where they drive the most value. We are hiring a Data Scientist III in Boston, MA and Bellevue, WA. You will work across the lifecycle of model development—ideation, analysis, architecture, prototyping, tuning, experimentation, and production deployment—while collaborating with MLEs and fellow data scientists to advance our technical capabilities. Join our high-achieving team and help shape the future of AI innovation at Chewy!

What You’ll Do:

• Lead end-to-end data science projects—from idea generation and offline evaluation to deployment, experimentation, feedback loops, and continuous model improvement.


• Formalize assumptions about model behavior, define and detect outliers systematically, and implement fixes or improvements where needed.


• Partner with marketing stakeholders to identify, scope, and prioritize AI opportunities that create measurable business impact.


• Rapidly prototype and test GenAI and agentic AI solutions to address high-impact marketing challenges.


• Build internal AI copilots and assistants that enhance productivity, automate repetitive workflows, and accelerate campaign execution.


• Stay ahead of industry and technology trends to anticipate emerging opportunities in AI and e-commerce.


• Communicate insights and recommendations clearly—both verbally and in writing—to audiences with varying technical expertise.


• Collaborate with a team of passionate data scientists and machine learning engineers working across AI, search, and personalization.

What You’ll Need:

• Advanced degree (M.S., Ph.D., or equivalent experience) in Data Science, Statistics, Applied Mathematics, Computer Science, Physics, or a related field, or 4+ years of experience designing optimization and machine learning solutions at scale.


• Strong foundation in machine learning and statistical methods, including Bayesian inference, regression, classification, forecasting, time series, causal inference, and machine learning techniques such as tree-based models (e.g., XGBoost, Random Forests), neural networks, and clustering methods.


• Ability to understand and apply advanced mathematics and data science methodologies to solve complex business problems.


• Hands-on expertise with LLMs and Generative AI frameworks (e.g., LangChain, Hugging Face, OpenAI, Anthropic, vector databases).


• Experience in rapid prototyping and experimentation, including prompt engineering, few-shot techniques, and fine-tuning LLMs for domain-specific tasks.


• Proficiency in Python (and ideally SQL/ML pipelines) for building proof-of-concepts and deploying production-ready solutions.


• Strong ability to translate business needs into AI opportunities, with knowledge of marketing workflows, customer data, and campaign lifecycles.


• Passion for emerging AI trends and the initiative to proactively surface and test new applications.


• Ability to thrive in ambiguous, fast-paced environments, structuring open-ended problems into actionable solutions.


• Excellent communication skills, with the ability to convey technical concepts to both technical and non-technical stakeholders.

Bonus (if applicable):

• Experience with at least one cloud services provider(AWS, GCP, Azure, etc.).


• Experience with ML platforms and frameworks, e.g., Databricks.


• Familiarity with reinforcement learning techniques

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