Description
As our PhD Intern, Applied Scientist - Optimization, you will support core innovation for our industry-leading products and services, taking part in a pivotal strategic initiative to transform our static optimization pipelines into a dynamic, self-learning platform. This transformation is critical to improving our operational efficiency and enabling our systems to intelligently adapt to real-time market trends.
The systems you contribute to will help manage millions of service appointments daily across thousands of our most critical global customers, powering a platform with over $1 billion in revenue. This is a unique opportunity to apply your academic research to a project driving measurable efficiency gains that translate directly into substantial customer value.
Your primary project will be to support research on applying state-of-the-art optimization methodologies to enhance our optimization service’s scalability, robustness, and dynamic capabilities. You will collaborate with senior engineers on how we research, design, and build systems that must balance the competing demands of predictive accuracy, computational scalability, and real-time decision-making.
This is a hands-on research internship focused on technical excellence and innovation. You will be mentored by a talented team of engineers and scientists, contributing to the end-to-end technical strategy from novel research to production deployment.
What You'll Do
Apply academic theory into practice by experimenting and prototyping solutions that showcase how novel algorithms can be used to address the challenges that our system faces today.
Contribute to the technical vision and long range plan for our optimization capabilities by conducting research and developing proofs-of-concept (POCs).
Participate in technical discussions that help set the strategic direction for the research and development of novel optimization solutions.
Collaborate with cross-functional teams to help translate prototypes into actionable solutions.
Collaborate closely with a team of engineers and scientists, fostering a culture of innovation and continuous learning.
Required Qualifications
Currently enrolled in a Ph.D. program in Operations Research, Applied Mathematics, Computer Science, or equivalent.
Strong research experience and foundational knowledge in solving complex optimization problems through mathematical programming or metaheuristics, with an interest in blending mathematical models and machine learning techniques to solve complex industrial problems.
Proficiency working with open-source and proprietary mathematical programming solvers such as CPLEX and Gurobi.
Experiences in one or more of the following areas: large-scale forecasting and data-driven decision making.
Strong communication and collaboration skills.
Nice to Have Qualifications
Experience with online learning or applying Reinforcement Learning (RL) specifically to optimization problems.
Familiarity with emerging research areas like differentiable optimization or learning-to-optimize (L2O) frameworks.
Solid grasp of probabilistic modeling, statistical evaluation, and the use of simulation-based testing to validate complex models and systems.
Proficiency in modern ML frameworks like PyTorch or TensorFlow.
Prior experience working within cross-functional teams that include data scientists, operations research specialists, and software engineers.