Finance Expert - Quant Research & Systematic Trading

Weekday AI; United States; Remote; USD 234,000 - 303,000; Full time

Job summary

Weekday AI is seeking experienced quantitative researchers and systematic trading professionals for a remote Finance Sprint. You will evaluate realism, accuracy, and practicality of finance workflows, reviewing confidential task scenarios and providing structured feedback to improve rigor and relevance.

Candidates should have 5–8 years (Researcher) or 8–15 years (Senior Reviewer) in quantitative research, trading, or related fields, with strong Python skills and attention to detail.

Qualifications

• 5–8 years of relevant experience for the Researcher Track, or 8–15 years for the Senior Reviewer Track.
• Professional experience in quantitative research or systematic trading, portfolio construction, or related quantitative investment disciplines.
• Strong Python programming skills with hands-on experience developing, validating, or reviewing quantitative research workflows.
• Excellent written communication and attention to detail with structured quality standards.

Responsibilities

• Review quantitative finance tasks and workflows for technical accuracy, realism, clarity, and internal consistency.
• Evaluate whether datasets, assumptions, constraints, and workflows reflect real-world quantitative research and trading practices.
• Identify missing assumptions, ambiguous instructions, unrealistic constraints, unsupported requirements, and edge cases.
• Assess whether tasks measure practical quantitative finance capabilities, not just theory.
• Provide concise, structured written feedback with actionable recommendations to improve task quality.
• Collaborate with subject matter experts to calibrate evaluations and maintain quality.

Skills

Quantitative research

Systematic or algorithmic trading

Portfolio construction

Quantitative portfolio management

Financial engineering

Data quality/validation

Python programming

Backtesting

Tools

Python (NumPy/SciPy)

Job description

This role is for one of our clients

Compensation: $170-$220 per hour

A leading AI research initiative is launching a specialized Finance Sprint focused on evaluating the realism and technical accuracy of quantitative finance workflows. We are seeking experienced quantitative researchers and systematic trading professionals to assess whether complex finance tasks accurately reflect real-world industry practices.

This is a high-impact, fast-paced engagement where your expertise will help ensure AI evaluation tasks measure genuine quantitative finance capabilities. The work involves reviewing confidential task scenarios, identifying weaknesses, and providing structured feedback to improve the realism, rigor, and practical relevance of each workflow.

Due to the collaborative nature of the sprint, candidates should be available to actively participate throughout the engagement and respond promptly to project updates and feedback.

Key Responsibilities

• Review quantitative finance tasks and workflows for technical accuracy, realism, clarity, and internal consistency.
• Evaluate whether provided datasets, assumptions, constraints, and expected workflows accurately represent real-world quantitative research and systematic trading practices.
• Identify missing assumptions, ambiguous instructions, unrealistic constraints, unsupported requirements, and critical edge cases.
• Assess whether tasks effectively measure practical quantitative finance capabilities rather than theoretical knowledge alone.
• Provide concise, structured written feedback along with actionable recommendations to improve task quality and realism.
• Collaborate with fellow subject matter experts to calibrate evaluations and maintain consistent quality standards across the project.

Core Qualifications

• 5–8 years of relevant experience for the Researcher Track, or 8–15 years for the Senior Reviewer Track.
• Professional experience in one or more of the following:
  • Quantitative Research
  • Systematic or Algorithmic Trading
  • Portfolio Construction
  • Quantitative Portfolio Management
  • Financial Engineering
  • Related quantitative investment disciplines
• Strong Python programming skills with hands‑on experience developing, validating, or reviewing quantitative research workflows.
• Deep understanding of:
  • Research methodology and bias
  • Data quality and validation
  • Backtesting best practices
  • Transaction costs and market impact
  • Risk modeling
  • Portfolio implementation constraints
  • Quantitative model evaluation
• Excellent written communication skills with the ability to clearly explain evaluation decisions and professional reasoning.
• Exceptional attention to detail, sound judgment, and the ability to consistently evaluate work against structured quality standards.
• Experience reviewing research, mentoring analysts, establishing quality controls, or approving technical work is highly valued for senior‑level reviewers.

Why This Role Matters

High‑quality AI systems require evaluation tasks that accurately reflect the complexity of real‑world quantitative finance. Your expertise will help ensure AI models are assessed against authentic industry practices, enabling more reliable benchmarking and meaningful performance improvements.

Equal Opportunity

We are committed to fostering an inclusive environment and welcome qualified applicants from all backgrounds. All employment decisions are made without regard to legally protected characteristics, and reasonable accommodations are available throughout the application and engagement process upon request.

Contract & Engagement Details

• Independent contractor engagement.
• Fully remote with flexible working hours.
• High‑engagement project with the potential for extension based on business needs and individual performance.
• Weekly payments are processed through supported payment platforms.
• Applicants must be legally authorized to work in the applicable hiring location. Unfortunately, visa sponsorship, including H‑1B and STEM OPT support, is not available for this opportunity.