Senior Data Scientist

About Wonder

Everything’s on the menu at Wonder. Except compromise.

The Wonder app is the premiere platform to feed every craving, all in one order. Our 25+ award-winning restaurant partners span every cuisine you can think of, from Greek to Thai, and come from the minds of the best chefs in the industry—Bobby Flay, José Andrés, Marcus Samuelsson, and more.

And our diners don’t have to choose just one: they can mix and match dishes from as many Made by Wonder restaurants as they’d like, or order from neighborhood gems near them. Everything is made to order at our brick-and-mortar locations across the East Coast and delivered fast and free, and more locations are opening every week.

The best in the business are coming to Wonder, working every day to make us the destination for every mealtime moment. Join a team of technology, culinary, and logistics pioneers, backed by top-tier venture capitalists, and help us make great food more accessible.

About the role

We’re looking for a Senior Applied Data Scientist, Operations Research, to lead and solve complex problems in our high-density restaurants and 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

• Develop and implement models and algorithms to solve core operational challenges in kitchen sequencing, order batching, and order release logic

• Design and execute "what-if" analyses and simulations to evaluate the impact of proposed changes on key metrics like expo sit time, courier wait time, and kitchen throughput

• Conduct exploratory research into optimization methodologies for our existing Kitchen Display System (KDS) simulator to achieve more globally optimal outputs

• Collaborate with engineering teams to translate optimization models into production-level code and ensure seamless integration with our KDS

• Model and account for real-world uncertainty in operational process times, leveraging probabilistic approaches to optimize kitchen operations

• Formulate multi-objective functions to optimize for both our customers and business

• Advise on the evolution of the KDS simulator to a stochastic modeling scheme and help incorporate forecasting for upcoming demand

• Partner with product and engineering teams to define data requirements and leverage new data sources, such as historical operational step times, to improve the accuracy of our models

The experience you have

• 3-5+ years relevant experience and MS in one of the following disciplines: Computer Science, Data Science, Applied Math, Operations Research or a related quantitative field OR PhD

• Demonstrated ability to domain model complex problems and build solutions iteratively, starting with minimum viable techniques and adding complexity only as needed

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

• Deep understanding of core principles and state-of-the-art techniques in Operations Research and Data Science

• 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 to production environments

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

• Nice to have:

  • Experience working on successful applied research projects in industry environments

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

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

  • Hands-on experience using predictive modeling in optimization and simulation modeling contexts, specifically in operations and supply chain applications

  • 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: $191,000 per year.

Wonder uses geographic-specific salary structures, which means the salary offered may vary depending on where the job is located. The final salary offer will take into account various factors, such as the candidate's skills, education, training, credentials, and experience.

Our hybrid model requires 3 days a week in the office. That said, many team members choose to come in more often to take advantage of in-person collaboration and connection. You're welcome—and encouraged—to be in the office up to 5 days a week if it works for you.

Benefits

We offer a competitive salary package including equity and 401K. Additionally, we provide multiple medical, dental, and vision plans to meet all of our employees' needs as well as many benefits and perks that are not listed.  

A final note 

At Wonder, we believe that in order to build the best team, we must hire using an objective lens.  We are committed to fair hiring practices where we hire people for their potential and advocate for diversity, equity, and inclusion.  As such, we do not discriminate or make decisions based on your race, color, religion, gender identity or expression, sexual orientation, national origin, age, military service eligibility, veteran status, marital status, disability, or any other protected class.  If you have a disability, please let your recruiter know how we can make your interview process work best for you.

We look forward to hearing from you! We'll contact you via email or text to schedule interviews and share information about your candidacy.