Our Opportunity:

Chewy is looking for a Staff Machine Learning engineer to join a science driven team in supply chain charged with delivering state-of-art ML models helping all pet parents and people.

In this role, you will lead development and deployment of our critical Machine learning and deep learning models, which includes guiding data scientists, platform engineers in the delivery of the models. This role will involve execution, strategy, partnership, architecture, mentoring, and more. You have high standards and will take pride in Chewy like we do as well as push us to be better. You have delivered challenging technical solutions at scale, with a particular focus in using machine learning techniques to drive business and customer impact. You are equally happy talking about deep learning and software engineering, as you are brainstorming about continuous improvement and technology career development.

What You'll Do:

• Solve impactful, large-scale ML problems and take the solutions from planning to production.
• Ideate and implement advanced ML solutions like domain-specific embeddings, custom featurization, and customized algorithms.
• Build a great company-wide ML platform that enables other data scientists and ML engineers to train, validate, deploy, and monitor ML
• Mentor teammates in advanced ML and ML engineering practices
• Engage with engineering leadership across Chewy to define development and production processes for ML creation and deployment.
• Recommend architectural patterns; e.g., consult on the architectural design of ML pipelines for collecting data for ML training and inference.
• Collaborate with a distributed team, including pairing, code reviews, identifying opportunities for code refactoring, and defining best practices.

What You'll Need:

• 8+ years of recent professional industry experience in data science/software engineering
• 6+ years writing production-level, scalable code (e.g. Python, Scala)
• Expertise in machine learning/Deep Learning and statistical modeling
• Excited to answer product/engineering questions with data
• Strong communication and collaboration skills
• Ability to write technical papers and present results to both technical and non-technical audiences
• Driven to proactively help their teammates and Indeed’s products
• Experience designing and conducting complex projects

Bonus:

• Ph.D. (preferred) or M.S. in a quantitative field (e.g., Quantitative Social Science, Computer Science, Statistics, Applied Mathematics, Physics, Machine Learning)
• Are familiar with recent developments in deep learning & machine learning, especially in time series, anomaly detection, NLP, explainability and interpretability, and the intersection thereof
• Have full stack experience in data collection, aggregation, analysis, visualization, productionization, and monitoring of data science products
• Are proficient in small data modeling work: Python, R, Julia, Octave
• Are proficient in big data modeling work: Hadoop, Pig, Scala, Spark
• Can fish for data: SQL, Pandas, MongoDB
• Deploy data science solutions: Java, Python, C++
• Communicate concisely and persuasively with technical and non-technical audiences