Software Expert (Scientific Computing)

Scientific discovery and data-driven insights increasingly rely on robust computational tools. As a Software Expert (Scientific Computing), you will empower AI to generate accurate, efficient, and reliable code for numerical methods, statistical analysis, and complex scientific simulations, accelerating research across disciplines.

Key Responsibilities

• Evaluate AI-generated code for scientific computing tasks, ensuring mathematical correctness and numerical stability.
• Review implementations using libraries like NumPy, SciPy, and Pandas for data manipulation, statistical analysis, and linear algebra.
• Assess AI's ability to generate code for advanced numerical methods, including differential equations, optimization, and signal processing.
• Create high-quality training data for statistical modeling, hypothesis testing, and data visualization in Python and R.
• Debug and optimize AI-generated scientific code for performance, memory efficiency, and parallelization.
• Provide expert feedback on AI's understanding of domain-specific scientific principles and best practices in computational science.

Ideal Qualifications

• 7+ years of experience in scientific computing, computational science, or quantitative research.
• Expert proficiency in Python with extensive experience using NumPy, SciPy, Pandas, and Matplotlib.
• Strong experience with R for statistical analysis and data visualization.
• Deep understanding of numerical methods, linear algebra, calculus, and statistics.
• Familiarity with parallel computing frameworks (e.g., Dask, MPI) and GPU acceleration (e.g., CuPy) is a plus.
• Experience with data visualization tools and libraries (e.g., Seaborn, Plotly).

Project Timeline

• Start Date: Within 1-2 weeks
• Duration: 6 months, with potential for extension
• Commitment: 20-30 hours/week