Staff Applied Data Scientist, Operations Research

About Us

Imagine: 30 unique restaurants to order from, brought to your door in under 30 minutes. That’s what our customers experience.

At Wonder, we are making world-class food within reach, no matter where you live. Tech drives our entire process from raw ingredients all the way to your door - this vertical integration provides a new standard of dining that allows you to enjoy menus from award-winning chefs and iconic restaurants across the country, all in one place. Our elevated brick + mortar locations offer pick up and dine in options, as well as delivery to your home.

As a food-tech startup backed by top-tier venture capitalists and led by a team of experienced entrepreneurs—including some of the most accomplished leaders in the technology, culinary, and logistics industries—we’re growing fast. Join us in pioneering a new category of dining — “Fast-Fine” — and revolutionizing the way people eat. You’ll work in a supportive and collaborative environment where our

culture and values — Mastery, Compassion, and Courage — are taken as seriously as delivering an incredible experience for our customers.

About the role

We’re looking for a Staff Applied Data Scientist, Operations Research, to lead and solve complex problems in our supply chain ranging from food production, logistics, cooking and final delivery to customers. Our team works with stakeholders from all over the company to solve complicated problems with cutting edge technology. Wonder strives to push the boundaries of what's possible, and our culture encourages continuous innovation and experimentation. Tech not only follows this, but enables the rest of the organization to do the same. Bring your knowledge and experience to our team culture, and influence it for the better!

Key Responsibilities

• Working closely with your product and engineering managers to design and build ML solutions that have a profound impact on the business.Code that is clean, well documented, and easy to iterate on.

• Eagerness to learn cross-functionally, not only within the ML domain but also within various business functions like Culinary, Operations, Consumer Product, Marketing, Finance, etc

• Openness to both giving and receiving constructive feedback with the intention of bettering everyone involved

• Thirst for learning and a passion for putting new technologies, processes and patterns into practice in a realistic manner

• Understanding how the whole system fits together at both a high level as well as at a detailed level in the given area of responsibility and beyond

The experience you have

• 5+ years relevant experience, MS in one of the following disciplines: Computer Science, Electrical or Computer Engineering, Data Science, Applied Math, Operations Research or a related quantitative field OR PhD

• Hands-on experience in neural networks, reinforcement learning, predictive modeling and other machine learning techniques and their application to supply chains

• Fluency in Python, SQL or similar scripting languages, knowledge of Java, Kotlin, C++, or other programming languages

• Deep understanding of state-of-the-art scientific principles and techniques in at least one computer science discipline

• Experience with mathematical optimization frameworks such as CPLEX, Gurobi, Xpress, OR-tools

• Experience defining and implementing solutions for difficult problems that require consideration of relevant tradeoffs

• Experience in deploying OR solutions in the cloud environment

• Strong communication and collaboration skills to work effectively with different stakeholders and cross-functional teams

• Self-driven with an eagerness to learn and apply the latest OR and AI techniques to solve complex business problems

Nice to have

• Experience working on successful applied research projects in industry environments

• Contributions to peer-reviewed publications that validate novelty in your field

• Experience applying LLMs, NLP/NLU to solve specific customer or business problems

• Track record of documenting and sharing findings in line with scientific best practices

• Experience with machine learning pipeline and data orchestration tools such as MLflow, Kubeflow Pipelines, Airflow, etc

• Experience explaining complex scientific concepts to team members

• Domain knowledge of comparable products (e-commerce, retail, supply chain)

• Experience applying and extending existing scientific techniques to address specific customer needs

Base Salary: $239,500 per year.

Benefits

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