Data Science Engineer - Algebraic Geometry & ML Pipelines

California Institute of Technology; Pasadena (CA); On-site; USD 46,000 - 63,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

Retirement savings plans

Generous paid time off

Job summary

California Institute of Technology in Pasadena seeks a Data Science Engineer to work with the PI and research group to develop computational and data-driven methods supporting research in algebraic geometry, computational algebra, and related areas of mathematics and scientific computing.

You will design and implement algorithms, symbolic computation systems, data processing pipelines, and machine learning, contributing to both research and production environments.

Qualifications

• Bachelor Degree in Computer Science, Statistics or Mathematics.
• Strong programming skills in Python and R.
• Background in algebraic geometry, computational algebra and symbolic computation.
• Experience with algorithm development and research.

Responsibilities

• Develop computational and data-driven methods supporting research in algebraic geometry, computational algebra, and related areas of mathematics and scientific computing.
• Contribute to the design and implementation of algorithms, symbolic computation systems, data processing pipelines, and machine learning.

Skills

Python

R

SQL

PyTorch

Education

Bachelor's degree in Computer Science, Statistics or Mathematics

Master's degree in Computer Science, Statistics or Mathematics

Tools

Containerization

Orchestration

Cloud computing

HPC platforms

Git

Job description

California Institute of Technology in Pasadena seeks a Data Science Engineer to work with the PI and research group to develop computational and data-driven methods supporting research in algebraic geometry, computational algebra, and related areas of mathematics and scientific computing.

You will design and implement algorithms, symbolic computation systems, data processing pipelines, and machine learning, contributing to both research and production environments.