Hire Remote Quantitative Analysts

Hire Quantitative Analysts Who Model Financial Risk with Mathematical Rigor

Quantitative analysis sits at the intersection of advanced mathematics, financial theory, and software engineering. The quants who can build production-grade pricing models, design risk management frameworks, and implement algorithmic trading strategies — and who understand both the mathematical rigor and the practical constraints of deploying these systems in production — are among the most specialized and valuable engineers in financial services.

We match you with senior Quantitative Analysts who’ve built pricing models, risk engines, and quantitative research systems for investment banks, hedge funds, asset managers, fintech companies, and insurance organizations. Engineers who combine graduate-level mathematical training with the software engineering skills to implement models that are correct, efficient, and maintainable.

Start in days, not months. Pay 50% less than equivalent US market-rate quant talent.

What Our Quantitative Analysts Build

Derivatives Pricing & Valuation Models

Options, futures, swaps, and exotic derivative pricing models: Black-Scholes variants, Monte Carlo simulation engines, finite difference methods, and calibration frameworks for term structure models (Hull-White, LMM). Implementation in Python, C++, and QuantLib.

Risk Management Systems

VaR (Value at Risk), CVaR, Expected Shortfall, Greeks calculation, scenario analysis, and stress testing frameworks. Market risk, credit risk, and operational risk models that meet regulatory requirements (Basel III/IV, FRTB, CCAR) and support trading desk risk management.

Algorithmic Trading & Strategy Research

Statistical arbitrage, factor model construction, backtesting frameworks, transaction cost analysis, and execution algorithm design. Research infrastructure for systematic strategy development with proper out-of-sample validation methodology.

Credit Risk & Scoring Models

PD (Probability of Default), LGD (Loss Given Default), and EAD (Exposure at Default) models for credit portfolios. IFRS 9 and CECL expected credit loss modeling. Alternative credit scoring using ML for fintech lending applications.

Portfolio Optimization & Factor Models

Mean-variance optimization, Black-Litterman portfolio construction, multi-factor risk model development, and portfolio attribution analysis. Equity long-short factor research and risk premia strategies.

Quantitative Analyst Technology Stack

Languages: Python (NumPy, SciPy, pandas), C++ (high-performance pricing), R (statistical research), MATLAB

Quantitative Libraries: QuantLib, TA-Lib, PyMC, statsmodels, scikit-learn, JAX

Financial Data: Bloomberg API, Refinitiv Eikon, FactSet, Quandl, market data feeds

Risk Platforms: Murex, Calypso, OpenGamma, custom risk engine development

Databases: kdb+/q (time-series), PostgreSQL, Arctic (pandas time-series storage), InfluxDB

HPC: CUDA (GPU acceleration for Monte Carlo), Numba, Cython, MPI for distributed pricing

Client Success Story: Fintech Lender — ML Credit Model Expands Addressable Market 40%

A Series C consumer lending fintech was relying on traditional FICO-based credit scoring, declining 48% of applicants who didn’t meet the FICO threshold but were statistically creditworthy based on alternative data signals. Our Quantitative Analyst built an ML-based credit risk model using alternative data: bank transaction patterns (income stability, spending discipline, balance trends), rental payment history, employment data, and behavioral signals from the loan application process. The model used gradient boosting with proper out-of-sample validation and bias testing across protected characteristics. Deployed into their underwriting system, the model approved 22% more applicants from the previously declined pool with a default rate 8% lower than the existing model. Addressable market expanded 40% without a material change in portfolio credit risk.

Client Success Story: Asset Manager — Factor Model Identifies $180M in Portfolio Improvement

A mid-sized equity asset manager was running a discretionary growth strategy with inconsistent performance attribution — they couldn’t determine which decisions were driving returns vs. random market exposure. Our Quantitative Analyst built a multi-factor risk model with 12 factors (value, momentum, quality, growth, size, volatility, and sector factors derived from their universe). Factor model output showed that 60% of their active return was attributable to unintended sector concentration rather than stock selection skill. Portfolio rebalancing to maintain intended factor exposures while preserving stock selection alpha produced a 1.4% improvement in annualized risk-adjusted returns on a $180M equity book. The factor model became the standard portfolio construction tool for all new strategies.

Why Companies Choose Our Quantitative Analysts

• Mathematical rigor: They understand the mathematics behind the models they implement — not just the API calls
• Production engineering: Their models deploy to production environments, not just research notebooks — correct implementation, numerical stability, and performance optimization
• Regulatory awareness: They understand the regulatory context (Basel, FRTB, CCAR, IFRS 9) for the models they build
• 50% cost savings: Senior quant talent at a fraction of US and London market rates
• Fast start: Most engagements begin within 1–2 weeks

Engagement Models

• Individual Quantitative Analyst — One senior quant embedded with your research or risk team for model development, research, or production implementation.
• Quant Research Pod — Senior quant researcher paired with a quantitative developer (implementation focus) for teams separating research and production engineering.
• Risk Engineering Teams — Multiple quants for financial institutions building or modernizing risk management infrastructure.
• Contract-to-Hire — Evaluate a quant’s mathematical depth and software engineering quality before committing long-term.

How To Vet Quantitative Analysts

Our vetting identifies quants who combine mathematical rigor with software engineering competence — not just one or the other.

1. Mathematical depth assessment — Stochastic calculus, probability theory, numerical methods, and linear algebra applied to financial problems. This is not a multiple-choice test — we assess derivation ability and intuitive understanding. Over 90% of applicants do not pass this stage.
2. Model implementation — Implement a specific pricing model or risk calculation from scratch. We evaluate numerical stability, edge case handling, and the software engineering quality of the implementation.
3. Research methodology — Given a strategy research question, how do they design the study? What are the pitfalls (overfitting, look-ahead bias, survivorship bias) and how do they guard against them?
4. Production context — How do they ensure their models are correct in production? What testing approach do they use? How do they handle model risk — the risk that a model is theoretically sound but wrong in practice?

What to Look for When Hiring Quantitative Analysts

Strong quants are mathematically deep and pragmatically grounded — they understand when model assumptions fail in practice.

What strong candidates demonstrate:

• They can derive the pricing formulas they implement — they’re not just calling QuantLib functions
• They design out-of-sample validation before starting model development — not as an afterthought when results look good
• They discuss model limitations and failure modes proactively — the scenarios where the model breaks down are as important as its expected performance
• They write production-quality code: tested, documented, and maintainable by another engineer

Red flags to watch for:

• Can run existing pricing models but can’t explain the mathematical foundations or assumptions
• Backtesting without out-of-sample validation, transaction cost adjustment, or proper walk-forward methodology — optimistic results that won’t survive live trading
• Treats model output as ground truth without discussing model risk and failure scenarios
• No software engineering practices: no tests, no documentation, models that only the original developer can maintain

Interview questions that reveal real depth:

• “Derive the Black-Scholes PDE from first principles. What are the key assumptions and which ones are most likely to fail in practice?”
• “Walk me through a quantitative model you built and deployed to production. What validation did you do, what went wrong in practice, and what did you learn?”
• “How would you design an out-of-sample validation framework for an algorithmic trading strategy to avoid overfitting? What are the specific pitfalls you’re guarding against?”

Related Services

• Data Scientists — ML-focused data scientists for fintech applications requiring predictive modeling without deep financial theory.
• Data Engineers — Data pipeline engineers who build the market data and financial data infrastructure quants depend on.
• AI Product Engineers — Engineers who productize quantitative models into scalable financial product features.
• Python Developers — Senior Python engineers for quantitative research infrastructure and production implementation.

Want to Hire Remote Quantitative Analysts?

We source, vet, and place senior Quantitative Analysts who combine graduate-level mathematical depth with the software engineering skills to deploy production-grade financial models — quants who build pricing engines, risk systems, and algorithmic strategies that are mathematically rigorous and practically robust.

Get matched with Quantitative Analysts →

Ready to hire Quantitative Analysts with the mathematical depth your models require? Contact us today and we’ll introduce you to senior quant analysts within 48 hours.