Math > Numerical Analyst
Salary National Average
| Low | Medium | High |
|---|---|---|
| 60500.0000 | 76280.0000 | 89500.0000 |
Change from Previous Year:
+3%
Supply and Demand
Candidate Supply: 217 Job Openings: 11,517
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Short Description:
A Numerical Analyst, also referred to as a Numerical Analyst Engineer or Computational Scientist, is a specialized professional who focuses on developing mathematical algorithms and computational methods to solve complex problems in various fields. Their work is instrumental in areas such as physics, engineering, finance, and computer science, where precise numerical solutions are required for practical applications.
Duties / Responsibilities:
- Formulate numerical models and algorithms to solve real-world problems.
- Develop and implement computational methods to solve mathematical equations and simulations.
- Analyze the accuracy, stability, and efficiency of numerical methods and algorithms.
- Collaborate with researchers and experts to apply numerical solutions to specific problems.
- Utilize software tools, programming languages, and high-performance computing to implement numerical algorithms.
- Investigate and resolve numerical instability or convergence issues in computational models.
- Optimize algorithms and codes for better performance and reduced computational time.
- Validate and verify numerical solutions through benchmarking and testing.
- Stay updated with advances in numerical methods, mathematical software, and hardware technologies.
- Communicate complex numerical concepts and solutions to non-technical stakeholders.
Skills / Requirements / Qualifications
- Education: Bachelor's, master's, or Ph.D. in mathematics, computer science, engineering, or a related field.
- Programming: Proficiency in programming languages like Python, C++, or MATLAB.
- Mathematics: Strong mathematical skills, including knowledge of calculus, differential equations, linear algebra, and numerical analysis.
- Critical Thinking: Problem-solving and critical thinking abilities for developing and troubleshooting numerical algorithms.
- Computing Techniques: Knowledge of high-performance computing and parallel processing techniques.
- Communication: Strong analytical and communication skills to work with interdisciplinary teams.
- Software Tools: Familiarity with numerical libraries and software tools like MATLAB, NumPy, or SciPy.
- Industry Knowledge: Experience in a specific domain or industry, such as computational physics, computational finance, or scientific computing, can be advantageous.
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