Modeling Workflow Specialist

Two Sigma; New York (NY); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Two Sigma is seeking a technically skilled researcher to accelerate CPU-based modeling workflows by leveraging GPU parallelism and modern tooling. The role involves re-engineering pipelines and collaborating with modelers to ensure correctness and throughput improvements.

You will أيضًا work with GPU infrastructure planning and translate domain needs into technical specifications. This position requires a strong background in quantitative modeling and a solid grasp of Python scientific stacks.

Qualifications

• BS or MS in Science, Technology, Engineering or Math.
• Minimum 1 year of experience; 2-10 years preferred.
• Deep experience with quantitative modeling platforms and tooling — you've built or maintained the systems that modelers use daily
• Understanding of the full modeling lifecycle: feature engineering, simulation, model training, validation, and production deployment
• Familiarity with Python scientific computing ecosystem (NumPy, Pandas, scikit-learn)
• Strong relationship-building skills — you'll spend significant time working directly with modelers

Responsibilities

• Analyze existing CPU-based modeling workflows to identify high-impact GPU acceleration opportunities
• Re-engineer modeling tooling and pipelines to exploit GPU parallelism
• Partner with modelers to validate that GPU-accelerated workflows maintain correctness and improve throughput
• Inform GPU infrastructure requirements based on actual workload characteristics
• Bridge between modeling teams and GPU specialists, translating domain requirements into technical specifications

Skills

Python (NumPy/Pandas)

Modeling workflows

Quantitative modeling

GPU profiling & optimization

Communication with modelers

Education

BS or MS in STEM

Tools

CuPy

RAPIDS

cuDF

Job description

You will take on the following responsibilities:

• Analyze existing CPU-based modeling workflows to identify high-impact GPU acceleration opportunities
• Re-engineer modeling tooling and pipelines to exploit GPU parallelism
• Partner with modelers to validate that GPU-accelerated workflows maintain correctness and improve throughput
• Inform GPU infrastructure requirements based on actual workload characteristics
• Bridge between modeling teams and GPU specialists, translating domain requirements into technical specifications

You should possess the following qualifications:

• BS or MS in Science, Technology, Engineering or Math
• Minimum 1 year of experience required; 2-10 years of experience preferred
• Deep experience with quantitative modeling platforms and tooling — you've built or maintained the systems that modelers use daily
• Understanding of the full modeling lifecycle: feature engineering, simulation, model training, validation, and production deployment
• Familiarity with Python scientific computing ecosystem (NumPy, Pandas, scikit-learn)
• Strong relationship-building skills — you'll spend significant time working directly with modelers

Preferred qualifications:

• Experience profiling and optimizing computational workloads
• Familiarity with NVIDIA GPU-accelerated scientific computing ecosystem (CuPy, RAPIDS, cuDF)
• Background in distributed computing or HPC