Quantitative Analyst VA
A senior quantitative analyst who builds pricing, risk and forecasting models from your data, tests them rigorously and turns the results into tools your trading, lending or investment team can use, so decisions rest on evidence rather than gut feel.
- Top 1% vetted talent
- No lock-in contracts
- Replacement guarantee
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Hourly rates · No lock-in contracts
Description
A Quantitative Analyst VA builds and tests statistical models for pricing, risk, forecasting and portfolio analysis. They can work with large datasets, backtest strategies, develop reusable modelling pipelines and turn technical outputs into practical tools for investment, lending or risk teams. Outsource Teams provides top 1% vetted talent with advanced quantitative experience, no lock-in contracts and a replacement guarantee.
Responsibilities
- Develop statistical and machine learning models for pricing, risk, forecasting or portfolio analysis.
- Clean, structure and validate large datasets from market feeds, databases and internal systems.
- Backtest strategies and models with rigorous out-of-sample and stress testing.
- Build reusable analysis pipelines in Python or R with version control and documentation.
- Monitor model performance in production and recalibrate when behaviour drifts.
- Prepare model documentation and validation reports for review and governance.
- Design dashboards and reports that make model outputs usable for non-technical teams.
- Research new data sources and methods that could improve model accuracy.
- Collaborate with developers to move models from research into production systems.
- Present findings to portfolio managers, risk leads or executives in clear terms.
Software & Tools
Requirements
- 5+ years in quantitative analysis, risk modelling, data science or algorithmic research in finance or insurance.
- Degree in Mathematics, Statistics, Physics, Engineering or Finance; a master's degree or CFA or FRM progress is preferred.
- Strong Python (pandas, NumPy, scikit-learn, statsmodels) or R, plus SQL for data extraction.
- Experience with time series analysis, regression, Monte Carlo simulation and optimisation.
- Familiar with Git, Jupyter and cloud platforms such as AWS or Azure for compute.
- Explains statistical results in plain English to decision-makers.
- Fast, stable home internet with backup power, and comfortable working the client's market hours.
Frequently Asked Questions
What types of models can they build?
They can develop models for pricing, risk, forecasting, portfolio analysis and other financial applications.
Can they test and monitor models?
Yes. They can backtest strategies, conduct stress testing, monitor live model performance and recalibrate models when performance changes.
What tools can they use?
They can work with Python, R, MATLAB, SQL, Bloomberg Terminal and Microsoft Excel.
How experienced are the candidates?
Candidates are expected to have at least five years in quantitative analysis, risk modelling, data science or algorithmic research within finance or insurance.
How much does it cost?
Rates are listed in USD at $15 per hour for entry-level talent, $20 per hour for intermediate and $28 per hour for senior talent. Both full-time and part-time options are available.
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