Datacor - Lead Scientific Programmer
Requirements
• Masters or PhD level in science or engineering, with outstanding academic performance. • Broad experience with applied math and numerical methods for solving science and engineering problems (e.g., matrix algebra, numerical methods for solving partial differential equations). • Earth sciences or civil engineering background, preferably including contaminant transport modeling. • Extensive C++ programming experience, ideally in scientific or engineering applications; familiarity with commercial or open-source numerical libraries and frameworks is a plus. • Proven ability to design and implement practical numerical solutions to real-world problems. • Good communications skills, must write effectively and enjoy interacting with other team members. • Experience developing and supporting Windows desktop applications is a plus, as is hands-on experience with data science tools such as MATLAB, R, or Tableau. • Experience applying AI/ML techniques (e.g., machine learning models, LLM-based tools, or AI-assisted development) to scientific or engineering software is a plus.
Responsibilities
• Participate in design discussions and all aspects of the application development across the full development lifecycle, not just the mathematical layer. • Own the numerical solutions and mathematical/computer science algorithms underlying GoldSim's simulation engine - from the theoretical foundation (equations, algorithm design) through to implementation in our C++ codebase. • Push practical, real-world improvements to GoldSim's numerical capabilities, and propose new features driven by everyday or discipline-specific customer needs. • Participate in design discussions across the full development lifecycle, not just the mathematical layer. • Model Building & Verification • Build GoldSim models that apply and exercise the platform's numerical capabilities - for QA and verification, as example models shipped with the product, and in direct support of customers. • Use hands-on model-building as a primary way to validate that new or improved algorithms behave correctly in practice. • Documentation & Knowledge Transfer • Document both the theoretical basis (equations, algorithm descriptions) and the practical implementation of GoldSim's numerical methods, so this knowledge isn't concentrated in one person. • Partner with the founding engineer to formalize existing modeling, QA, and support methodology. • Collaboration & Support • Work alongside the software engineering team to integrate new or improved numerical capabilities into the broader platform. • Translate business and product requirements into technical specifications. • Serve as a technical resource for customer support and QA on advanced modeling and numerical questions. • Communicate technical/mathematical concepts clearly to engineers, QA, and product stakeholders. • Growth & Modernization • Bring curiosity for modern computational approaches - including applying machine learning to accelerate, approximate, or extend traditional numerical simulation methods. • Help identify opportunities to modernize GoldSim's existing algorithmic infrastructure.
Benefits
• EOE Statement:
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