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TuringVerified Job Source

Remote Quantitative Analyst (Finance)

Evaluate and train AI models on quantitative finance topics including stochastic modeling and derivatives pricing. Collaborate with researchers to create rubrics and benchmarks for assessing model capabilities.

  • Remote
  • Canada
  • Posted Jul 31, 2026
  • 1 position

Job summary

About Turing: Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L. Role Overview: Turing is looking for Quantitative Finance professionals to work with our researchers to improve the performance of AI models. You will apply your expertise in quantitative modeling, statistical analysis, algorithmic strategy development, and financial engineering to evaluate and train AI systems. If you enjoy solving complex quantitative problems and are interested in shaping the future of AI in finance, please apply. No prior AI experience is required. What Does Day-to-Day Look Like: Evaluate LLM models on quantitative finance topics such as stochastic modeling, derivatives pricing, statistical arbitrage, and risk quantification. Create rubrics to assess model capabilities on tasks like options pricing, Monte Carlo simulation, factor model construction, and backtesting methodologies. Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation strategies, and benchmarks. Requirements: 2+ years of experience in Quantitative Finance (e.g., quant trading, quant research, financial engineering, or risk modeling). Strong grasp of stochastic calculus, statistical modeling, derivatives pricing theory, and programming languages such as Python, R, or C++. Excellent English written communication. Bonuses (Not at All Necessary): CFA, FRM, CQF, Ph.D. in a quantitative field, or MBA in Finance. Perks of Freelancing with Turing Work on the cutting edge of AI and finance. Fully remote and flexible work environment. Competitive hourly compensation of ~$100+/hour depending on experience. Offer Details: Commitment: Flexible, 10–30 hrs/week. Duration: ~1 month, with the possibility of extension based on performance and project needs. After applying, you will receive an email with a login link. Please use that link to access the portal and complete your profile. Know amazing talent? Refer them at turing.com/referrals, and earn money from your network.

What you’ll do

Evaluate and train AI models on quantitative finance topics including stochastic modeling and derivatives pricing. Collaborate with researchers to create rubrics and benchmarks for assessing model capabilities.

Requirements

Requires 2+ years of experience in quantitative finance, risk modeling, or financial engineering. Must have a strong grasp of stochastic calculus, statistical modeling, and programming in Python, R, or C++.

Benefits

• Remote and flexible work environment

Other relevant skills

Identified from the job description. Confirm important requirements above.

  • Quantitative Modeling
  • Statistical Analysis
  • Algorithmic Strategy Development
  • Financial Engineering
  • Stochastic Calculus
  • Derivatives Pricing
  • Python
  • R
  • C++
  • Risk Quantification
  • Monte Carlo Simulation
  • Factor Model Construction
  • Backtesting Methodologies

Job areas

  • Finance & Accounting
  • Data & Analytics
  • Science & Research
  • Technology
  • Software

Additional details

Minimum education
Master’s degree
Minimum experience
2+ years
Posting language
English
Working hours
30 hours per week
Seniority
Entry level
Application method
Direct apply is available