Benchmark Designer in Computational Statistics & Applied Math

Benchmark Designer in Computational Statistics & Applied Math

Full-Time 29700 - 36300 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Design graduate-level problems for a large-scale AI benchmark using real scientific software.
  • Company: Obsidian, a leader in computational statistics and applied mathematics.
  • Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
  • Other info: Join a forward-thinking team dedicated to advancing AI research.
  • Why this job: Shape innovative benchmarks and collaborate with experts in a dynamic field.
  • Qualifications: Expertise in computational statistics, applied mathematics, and proficiency in R/Python.

The predicted salary is between 29700 - 36300 Β£ per year.

Obsidian seeks a Computational Statistics and Applied Mathematics Expert to design graduate-level problems for a large-scale AI benchmark.

You will craft tasks requiring real scientific software and multi-step workflows, including simulations and result interpretation.

You will work with statisticians and applied mathematicians to shape problems that test reasoning under research conditions, leveraging R/Python packages and reproducible concepts.

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Benchmark Designer in Computational Statistics & Applied Math employer: Obsidian

At Obsidian, we pride ourselves on fostering a collaborative and innovative work culture that empowers our ML Scientists to push the boundaries of machine learning research. Located in a vibrant tech hub, we offer competitive compensation, flexible project-based work, and ample opportunities for professional growth, making us an excellent employer for those looking to make a meaningful impact in the field of AI.

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Contact Details:

Obsidian Recruitment Team

We think you need these skills to ace Benchmark Designer in Computational Statistics & Applied Math

Computational Statistics
Applied Mathematics
Problem Design
AI Benchmarking
Scientific Software Proficiency
Multi-step Workflow Development
Simulations