> Markdown version of [/jobs/ext/2708867-data-scientist](https://www.wearedevelopers.com/jobs/ext/2708867-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** fuse - **Location:** San Leandro, United States - **Contract:** Permanent contract - **Skills:** Adobe Flash, C++ (Programming Language), Fortran (Programming Language), Python (Programming Language), NumPy, SciPy, PIC Microcontroller, Pytorch, Slurm, Software Version Control, Data Pipelines - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/data-scientist-f-energy-9441862 ## About the Role * PhD in Physics, Applied Mathematics, Plasma Physics, Computational Science, Nuclear Engineering, or a closely related field. An exceptional Master's candidate with a strong publication record will be considered. * Strong academic record, with a target GPA of 3.8+ from a competitive program. * Demonstrable depth in at least one of: magnetohydrodynamics, kinetic plasma theory, radiation transport, or pulsed-power circuit modeling. Familiarity with the others. * Fluency in vector and tensor calculus, Maxwell's equations in arbitrary geometries, hyperbolic PDEs and numerical schemes, Bayesian statistics, and optimization under constraints. * Production-grade Python using NumPy, SciPy, xarray, JAX, or PyTorch for differentiable physics, with comfort dropping into C++ or Fortran when Python isn't fast enough. * Experience writing simulation code from scratch, not just running someone else's solver. * Ability to read a physics paper, identify the load-bearing assumption, and determine quickly whether it applies to our regime., * Hands-on experience with one or more of: MCNP, GEANT4, FLUKA, PIC codes such as LSP, EPOCH, or WarpX; MHD codes such as USim, FLASH, or HYDRA-class; or DPF-specific codes such as Lee model or GORGON-adjacent work. * Prior work fitting models to noisy experimental diagnostics, including neutron TOF, X-ray spectroscopy, or magnetic-probe arrays. * HPC experience including SLURM, MPI, GPU acceleration, and profiling at scale. * Familiarity with the DPF literature or adjacent fields such as Z-pinch, MagLIF, or ICF, and a track record of getting up to speed quickly. * Strong software-engineering hygiene: version control, tests for numerical code, and reproducible environments. ## Description You will build our in-house simulation stack - the models that predict what our fusion machines will do, and the inference machinery that pulls physics out of shot data. This is not a "fit a curve to a dashboard" data science role., * Build a coupled simulation framework for our DPF: pulsed-power circuit, sheath formation and run-down (snowplow / slug / Lee-type models, progressing toward MHD), pinch and instability development, and neutron and X-ray production. * Implement radiation source-term models: thermonuclear vs. beam-target neutron-yield decomposition, bremsstrahlung and line-emission spectra, and anisotropy. * Develop surrogate and reduced-order models so designers and physicists can iterate on parameter sweeps without standing up an HPC job each time. * Build the data pipeline that ingests every shot's diagnostic stream, including Rogowski coils, B-dots, silver activation, time-of-flight neutron detectors, filtered diodes, and fast cameras, and joins it to predicted output for systematic residual analysis. * Quantify uncertainty seriously: Bayesian inference over model parameters, identifiability analysis, and honest error bars on yield predictions. * Stand up simulations of supporting machine outputs, including anode/cathode lifetimes, electrode erosion models, and capacitor-bank aging, so operations decisions are informed by physics rather than vibes. * Publish internally with the same standards you'd publish externally: derivations written out, assumptions stated, code reviewed. * Eventually grow a small simulation team. For now, you will be a force multiplier of one., * Take responsibility. If something is broken and you can fix it, fix it. See things through. * Move fast. Make decisions, learn quickly, and keep moving forward. * Think bigger. Simplify relentlessly. Don't default to the safe answer. * Disagree and commit. The best idea wins. Once a decision is made, we move. * Desire to win. This is hard. Expect intensity. We are here to win. * Plan B is to make Plan A work. ## Related Videos - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Vectorize all the things! 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