Data Scientist - Simulation (Senior - Principal)

Data Scientist – Simulation (Senior – Principal)

We are seeking an experienced Senior or Principal Data Scientist to lead the development of advanced simulation models that power next-generation robotic warehouse systems. In this role, you will build high-fidelity simulations of large-scale robotic fleets, optimize system performance, and inform strategic product and operational decisions.

This is a highly cross-functional position spanning data science, robotics, operations research, and distributed systems, where your work will directly impact efficiency, throughput, and scalability of real-world automation systems.

We design, test, and deploy advanced robotic systems that improve warehouse throughput, efficiency, and reliability at scale. By reducing reliance on physical testing, we accelerate innovation cycles and drive significant operational savings and productivity gains across real-world production environments. Our team tackles complex automation and optimization challenges alongside world-class engineers and scientists, with the opportunity to directly influence cutting-edge systems deployed in the field.

Design and develop simulation frameworks for robotic warehouse systems, including robot fleets, inventory flows, task allocation, and human-robot interaction.

Build discrete-event and agent-based simulations to model complex, stochastic environments at scale.

Develop predictive and prescriptive models to optimize throughput, latency, and resource utilization.

Partner with robotics, software, and operations teams to:

Evaluate new algorithms (routing, task assignment, scheduling).

Test system changes before production deployment.

Identify bottlenecks and failure modes.

Create digital twins of warehouse environments to enable scenario testing and capacity planning.

Apply machine learning and statistical techniques to improve simulation realism and calibration.

Deliver clear insights and recommendations to technical and executive stakeholders.

Establish best practices for model validation, experimentation, and reproducibility.

Required Qualifications:

MS or PhD in Computer Science, Data Science, Operations Research, Applied Mathematics, Physics, or related field.

Minimum of 5 years (Senior) or minimum of 8 years (Principal) of experience in:

Simulation, modeling, or systems optimization.

Complex, distributed systems.

Strong experience with:

Discrete-event simulation (DES) or agent-based modeling.

Python (NumPy, Pandas, SciPy) and/or simulation frameworks (e.g., SimPy, AnyLogic, Arena, or custom tools).

Solid understanding of:

Probability, stochastic processes, and statistics.

Optimization techniques (LP, MIP, heuristics, metaheuristics).

Experience working with large datasets and building data pipelines.

Ability to translate real-world system behavior into computational models.

Preferred Qualifications:

Robotics, warehouse automation, logistics, or supply chain systems.

Fleet optimization or multi-agent systems.

Reinforcement learning for decision-making.

Path planning, task allocation, and scheduling algorithms.

Digital twin architecture.

Experience with C++ or high-performance systems for large-scale simulation.

Knowledge of cloud platforms (AWS, Azure, GCP) and distributed computing.

Background in experimentation platforms or A/B testing in operational systems.

Principal-Level Expectations (in addition to above):

Define and drive the long-term simulation and modeling strategy.

Architect scalable simulation platforms used across the organization.

Influence product and operational strategy through data-driven insights.

Mentor and grow a team of data scientists and engineers.

Serve as a subject matter expert in simulation, optimization, and system modeling.

Tech Stack:

Python (NumPy, Pandas, SciPy, SimPy).

Data platforms (Snowflake, Databricks).

Visualization (Grafana, Tableau).

Cloud infrastructure (GCP).

Optional: Python, C#, and C++.

Up to 10% of travel may be required. Employees must have a valid driver's license and the ability to drive and/or fly to client and other customer locations.

The employee is responsible for owning a credit card and managing expenses personally to be reimbursed on a bi-weekly basis.