Senior Data Scientist

Lab37•Pittsburgh, PA

10d•$159,000 - $201,000•Onsite

About The Position

Lab37 is building a digital twin capability to transform how we design and scale robotic food production systems. In this role, you will create simulations that demonstrate how robot configurations, new modules, workflows, and operating policies will perform under real-world conditions. Beginning with our Bowl Builder and expanding across the full kitchen, your models will help teams explore possibilities, understand tradeoffs, and make confident decisions about what to build next. You will own the technical direction for simulation at Lab37, including choosing tools, setting modeling standards, characterizing empirical model inputs, validating those models against operating data, and making simulation part of our product development process. Working closely with our hardware and robotics teams in Pittsburgh, you will turn complex physical systems into trusted decision-making tools that shape our product roadmap and improve kitchen performance. This is a staff-level individual contributor role with the opportunity to establish a capability that becomes central to how Lab37 designs, tests, and deploys its systems.

Requirements

Bachelor's degree in Industrial Engineering, Operations Research, Statistics, Computer Science, or a related quantitative field; advanced degree a plus.
3+ years of relevant work experience in simulation, industrial engineering, operations research, or data science.
Hands-on discrete-event simulation experience (e.g., AnyLogic, Simio, SimPy, Arena, or custom engines). This is the core of the role.
Track record of models that drove real design or operational decisions, not analysis alone.
Strong analytical and statistical rigor, with the ability to model complex, constraint-driven systems end to end and validate them against imperfect real-world data.
Strong programming skills (e.g., Python, SQL) and the ability to build and maintain simulation tooling.
Comfort partnering directly with hardware and robotics engineers and working from telemetry and operational data.

Nice To Haves

Experience building digital twins of physical or manufacturing systems.
Experience with robotics, automation, or manufacturing and production environments.
Queueing theory, throughput and bottleneck analysis, or capacity planning.
Experience working with real-time operational or telemetry data in production environments.
Experience in food service, on-demand delivery, or just-in-time manufacturing / production systems.

Responsibilities

Build discrete-event simulation models of the Bowl Builder and other robots, covering dispensing, timing, module configuration, and throughput under realistic demand.
Characterize the stochastic inputs the models depend on, deriving distributions for order arrivals, dispense timing, error and failure rates, and driver arrival times from operational data.
Extend beyond a single robot toward a digital twin of the broader kitchen ecosystem, including multi-station flow, prep, and labor.
Evaluate “build versus buy” across commercial platforms such as AnyLogic and Simio as well as in-house approaches, consolidate existing efforts, and set the modeling standards.
Validate models against real operational and telemetry data, including working through known gaps in sensor fidelity, so that predictions are trusted enough to act on.
Run configuration and policy experiments that are slow, costly, or risky to test on live systems.
Quantify throughput SLAs and performance targets for proposed robots and modules, directly informing which hardware we build next.
Support operational questions such as equipment sizing, bottleneck analysis, and kitchen configuration tradeoffs.
Partner closely with hardware and robotics engineering to ground models in how the machines actually behave, and feed results back into design decisions.
Translate simulation results into clear, decision-ready recommendations.
Communicate complex modeling to technical and non-technical stakeholders, with explicit assumptions and uncertainty.