What are the responsibilities and job description for the Lead Data Scientist position at Nastech Global?
Position: Lead Data ScientistLocation: Mountain View, CA - HybridDuration: 9 Months with possible extensions Main skills: Python (NumPy/SciPy/CuPy), C , PyTorch, Geostatistics, 3D Mathematics, CUDA/OpenMP, AI-assisted coding Simulation & Generative Modeling:We're seeking a Simulation Engineer with deep expertise in scientific computing, procedural generation, or computational physics to build the core algorithms for our 3D subsurface modeling engine. The Role:This is an implementation-heavy position bridging procedural physics and generative ML. You'll translate complex mathematical logic and latent-space models into performant code, solving high-dimensional geometric problems at scale. Scientific Software Engineer or Computational Scientist with a niche background in scientific simulation, procedural generation, or computational physics. This is an implementation-heavy role requiring a developer who can translate complex mathematical logic and generative ML models into performant code to solve high-dimensional geometric problems. What We're Looking For:Core Competencies:Procedural Generation: Terrain synthesis, voxel engines, noise-driven systemsScientific Computing: CFD, FEA, multi-physics solversComputational Geometry: 3D mesh processing, volumetric data structures, spatial partitioning Key Responsibilities:Algorithmic Implementation — Design memory-efficient algorithms for massive 3D voxel arrays and sparse data structures; implement deterministic and stochastic geometric rulesExample: Build C /Python kernels using 3D Perlin/Simplex noise and vector fields to simulate braided river systemsExample: Implement Boolean CSG algorithms for volumetric injections of igneous bodiesGenerative ML Engineering — Architect and train models (GANs, Diffusion) for high-resolution 3D spatial data using PyTorchExample: Generate realistic fracture networks via 3D generative modelsExample: Apply neural style transfer to map sedimentary textures onto volumetric frameworks Required Technical Skills:Languages: Expert Python (NumPy/SciPy/CuPy); proficient C for performance kernelsMathematics: Linear algebra, vector calculus, coordinate transformationsML Frameworks: PyTorch (generative AI, computer vision)Performance: CUDA/OpenMP; parallel computing experienceWorkflow: AI-assisted coding for rapid prototyping and testing Domain Knowledge:Mathematical maturity in:Structural modeling (Boolean operations, volumetric intersections)Sedimentology (layer stacking, erosion, flow simulation)Tectonics (displacement fields, kinematic transformations)Geostatistics (particle systems, stochastic models) Ideal Background:MS/PhD in Computer Science, Applied Mathematics, Computational Physics, or equivalentPortfolio/GitHub demonstrating procedural world-building, physics engines, or scientific simulators