$100-$300/hr quantitative finance and trading work, on your schedule
Review a model's quant models and derivations the way you'd vet research before it touches capital. Flag the overfit backtest, the look-ahead bias, the derivation that breaks under real assumptions.
Trusted by top research companies
Hi, we're Zac and Jack, the founders of Terac. We want to talk to you directly, because you are the most important part of what we're building.
Terac is a community of experts. People who have spent years getting good at something specific and hard. The world is about to need more of you, not less. As AI takes on more of the world's work, the bottleneck shifts to the people who actually know what they're talking about.
Expert labor is the rarest resource in the world right now, and it is shockingly hard to find. The companies that need a quant's eye on an overfit backtest spend weeks chasing people, paying placement fees, and settling for whoever is available. Meanwhile thousands of qualified people are sitting with knowledge that no one ever asks for.
That gap is what we're here to close. Every project that lands on Terac is routed to the people who actually know the answer, on their schedule, paid fairly, and only when the work is verified. No middleman taking a cut of your time. No vague gigs. No chasing checks.
We care about every single person in this community. If you join Terac, you're not a row in a database to us. We read the feedback. We answer the emails. We will fight for you when a customer is being unreasonable, and we will be honest with you when something on our side is broken. The quality of this panel is our entire company, and we owe you a serious bar.
If you've made it this far, here is what we're asking: claim your profile. Put your expertise on the record. Let the world's most ambitious teams come find you for the work only you can do.
Quantitative Finance questions
Still curious? Write to us at support@terac.com.
Deep specialization is exactly what clients want. Tasks match your sub-domain, so a rates vol quant reviews swaption pricing and term-structure models, not generic finance questions. The narrower your expertise, the more valuable your correction of an AI output tends to be.
The work is analytical and educational: you evaluate reasoning quality, annotate worked derivations, or critique a model's explanation of Greeks or VaR. You're not providing regulated advice, managing client assets, or generating live signals, so securities scope-of-practice limits don't apply to these tasks.
Both. Common deliverables include step-by-step derivations (Black-Scholes PDE, stochastic calculus proofs, Monte Carlo convergence), Python or C++ pricing-engine snippets, and written explanations of CVA, DV01, or expected shortfall. You're told the deliverable type before accepting, so you can pass on anything outside your skill set.
Yes. The CFA signals credibility, and quantitative portfolio construction is an active demand area, covering factor-model critique, covariance estimation, and optimization constraint review. Your Python fluency is a plus because many tasks involve annotating code alongside the financial reasoning.
Opportunities match on demonstrated expertise in the task's subject, not credential hierarchy. An FRM with hands-on credit risk modeling, Basel III capital, or stress testing is well-positioned for those areas. Academic credentials help for theoretical derivations, but practitioner depth often outperforms them on real-world risk frameworks.
Why your expertise matters
Today's quant AI conflates risk-neutral pricing with the real-world measure, misapplies Ito's lemma, and writes hedging strategies that ignore liquidity and funding costs. Your eye for whether reasoning is financially coherent, not just mathematically plausible, is what a textbook can't give it. Get this wrong and the next model is confidently incorrect.
How pay works
Top of the band goes to niche depth: exotic derivatives pricing (barrier options, vol surface, CVA/DVA), systematic strategy development, or regulatory capital under Basel III/IV or FRTB. Work is remote and asynchronous. You complete a defined task and payment releases once the deliverable is verified. No retainers, no minimums.
What the work looks like
A sample of the quantitative finance and trading work you would pick up. Every project is scoped, remote, and paid on verified completion.
- Review a model's Black-Scholes derivation for errors in the replication argument and flag any conflation of physical and risk-neutral measures.
- Evaluate a model's delta-hedging strategy for a long gamma position and assess whether rebalancing frequency and cost assumptions are realistic for the actual bid-ask spread.
- Annotate AI explanations of yield curve construction, correcting bootstrap sequencing and clarifying when interpolation choices move par-swap pricing.
- Build a worked Fama-French three-factor regression from daily returns, including correct treatment of look-ahead bias in factor portfolio formation.
- Score a batch of AI backtests for overfitting red flags: thin out-of-sample periods, survivorship bias, and Sharpe ratios that ignore return autocorrelation.
- Draft a reference explanation of initial margin under SIMM, at the level a senior quant uses to onboard a junior analyst, as training data.
Specialties we match
Quantitative Finance projects span a wide range of focus areas. Tell us where you go deep and we route the work that fits.
- Options pricing and Greeks
- Stochastic calculus (Ito, SDE)
- Volatility surface modeling (SVI, SABR)
- Factor models (Barra, PCA, statistical)
- VaR and CVaR / risk attribution
- Algorithmic and systematic strategy development
- Fixed income and rates modeling (HJM, LMM)
- CVA/DVA/XVA
- Market microstructure and execution
- FRTB / Basel III capital rules
- Backtesting and performance attribution
- Python / C++ / QuantLib
