22 of 22 Monte Carlo Method Jobs

Trading Systems Architecture Architect end-to-end front-to-back trading workflows, from deal capture and position management through risk analytics (VaR, Greeks, Monte Carlo, stress testing), P&L attribution, settlement, invoicing, confirmations and regulatory reporting (REMIT, EMIR, MiFID II, ACER). Design and govern … Proven track record of defining and delivering enterprise-level architecture for front-to-back trading platforms, including trade capture, risk management (VaR, Greeks, Monte Carlo, stress testing), P&L attribution, settlement, confirmations and regulatory reporting. Expert understanding of integration architecture in high-performance trading environments including ...

models for sports betting products Creating and enhancing pricing models, market derivations, and quantitative frameworks across multiple sports Applying complex statistical distributions and Monte Carlo simulation techniques to solve challenging modelling problems Leading BetBuilder and quantitative modelling initiatives from concept through to production deployment Backtesting, validating … strong track record of delivering impactful models Deep expertise in mathematical and statistical modelling techniques Strong understanding of probability theory, statistical distributions, and Monte Carlo simulations Advanced Python skills for modelling, analysis, and data manipulation Experience with model validation, backtesting, and performance evaluation Strong understanding ...

managers, hedge funds, and other sophisticated market participants. Responsibilities: Influence the product roadmap for portfolio risk analytics features, including Analytical/Historical/Monte Carlo VaR, factor-based performance attribution, and stress/scenario testing. Collaborate with quantitative developers to design, validate, and implement factor risk … . 6+ years’ experience in market risk analytics, preferably in product management, quantitative analysis, or risk technology roles. Strong understanding of Historical/Monte Carlo VaR, factor attribution, stress testing, and related statistical/financial methodologies. Experience with derivatives pricing models, stochastic processes, portfolio construction/ ...

managers, hedge funds, and other sophisticated market participants. Responsibilities: Influence the product roadmap for portfolio risk analytics features, including Analytical/Historical/Monte Carlo VaR, factor-based performance attribution, and stress/scenario testing. Collaborate with quantitative developers to design, validate, and implement factor risk … . 6+ years’ experience in market risk analytics, preferably in product management, quantitative analysis, or risk technology roles. Strong understanding of Historical/Monte Carlo VaR, factor attribution, stress testing, and related statistical/financial methodologies. Experience with derivatives pricing models, stochastic processes, portfolio construction/ ...

code basics (Terraform) Streaming and big data exposure (Kafka, Kinesis, Spark, Flink) is a plus Data quality and observability tooling (Great Expectations, Monte Carlo, Soda, dbt tests) is a plus Engineering & Soft Skills: Strong software engineering discipline—pipelines are products, not scripts Data quality mindset ...

Python skills and experience working in modern cloud-based data environments. Hands-on experience with data observability tools such as Grafana, Monte Carlo, or Acceldata, and data governance/quality platforms like Informatica, Collibra or Microsoft Purview is highly desirable. Experience within the Azure ecosystem (data ...

Python skills and experience working in modern cloud-based data environments. Hands-on experience with data observability tools such as Grafana, Monte Carlo, or Acceldata, and data governance/quality platforms like Informatica, Collibra or Microsoft Purview is highly desirable. Experience within the Azure ecosystem (data ...

Python skills coupled with experience working in modern cloud-based data environments. Hands-on experience with data observability tools such as Grafana, Monte Carlo, or Acceldata, and data governance/quality platforms like Informatica, Collibra or Microsoft Purview is highly desirable. Experience within the Azure ecosystem ...

Python skills and experience working in modern cloud-based data environments. Hands-on experience with data observability tools such as Grafana, Monte Carlo, or Acceldata, and data governance/quality platforms like Informatica, Collibra or Microsoft Purview is highly desirable. Experience within the Azure ecosystem (data ...

Python skills and experience working in modern cloud-based data environments. Hands-on experience with data observability tools such as Grafana, Monte Carlo, or Acceldata, and data governance/quality platforms like Informatica, Collibra or Microsoft Purview is highly desirable. Experience within the Azure ecosystem (data ...

clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms. Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best practices (testing, CI/CD, reproducibility), and develop team capability. Communicate complex quantitative concepts to non‐technical audiences; influence … curve building (multi‐curve frameworks), volatility surface modelling/calibration (e.g., SABR, Heston, local/stochastic volatility), and numerical methods (PDE/FDM, Monte Carlo, adjoint/automatic differentiation). Experience with market risk, time‐series/stress analytics, model risk governance, and regulatory expectations ...

clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms. Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best practices (testing, CI/CD, reproducibility), and develop team capability. Communicate complex quantitative concepts to non-technical audiences; influence … curve building (multi-curve frameworks), volatility surface modeling/calibration (e.g., SABR, Heston, local/stochastic volatility), and numerical methods (PDE/FDM, Monte Carlo, adjoint/automatic differentiation). Experience with market risk, time-series/stress analytics, model risk governance, and regulatory expectations ...

skills Cloud: GCP Warehousing: BigQuery/Snowflake Orchestration: Airflow Backend: Python, SQL, Shell Languages: Scala/Java DevOps: Terraform, Docker, Kubernetes Observability: Monte Carlo BI: Looker AI/ML: MLOps London | 12 Months Contract |£400 a day Inside IR35 If you want to work ...

skills in: Python (essential) C++/Java/C# (strongly preferred) Experience with: Object-oriented design and software engineering best practices Numerical methods (Monte Carlo, PDEs, stochastic calculus) Data structures, algorithms, and performance optimisation Familiarity with: Quant libraries (e.g., QuantLib) Cloud platforms or distributed computing (nice ...

skills in: Python (essential) C++/Java/C# (strongly preferred) Experience with: Object-oriented design and software engineering best practices Numerical methods (Monte Carlo, PDEs, stochastic calculus) Data structures, algorithms, and performance optimisation Familiarity with: Quant libraries (e.g., QuantLib) Cloud platforms or distributed computing (nice ...

skills in: Python (essential) C++/Java/C# (strongly preferred) Experience with: Object-oriented design and software engineering best practices Numerical methods (Monte Carlo, PDEs, stochastic calculus) Data structures, algorithms, and performance optimisation Familiarity with: Quant libraries (e.g., QuantLib) Cloud platforms or distributed computing (nice ...

C++ , and/or Python for numerically intensive code. Java and pricing engine experience is highly preferred. Numerical Expertise: Implement advanced methods including Monte Carlo, Tree/lattice, and PDE approaches. Analytics & Curves: Build core libraries for valuation, sensitivities, and XVA, alongside robust curve construction/ ...

Library Evolution: Build and evolve core analytics libraries used for valuation, sensitivities, scenarios, and XVA. Advanced Numerical Implementation: Implement advanced numerical techniques including Monte Carlo methods (with variance reduction), Tree/lattice methods, PDE approaches, and curve construction (bootstrapping/interpolation). High-Performance Engineering: Deliver ...

topics such as stochastic modelling, 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 back-testing methodologies. Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation ...

topics such as stochastic modelling, 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 back-testing methodologies. Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation ...

topics such as stochastic modelling, 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 back-testing methodologies. Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation ...

topics such as stochastic modelling, 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 back-testing methodologies. Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation ...

topics such as stochastic modelling, 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 back-testing methodologies. Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation ...

topics such as stochastic modelling, 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 back-testing methodologies. Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation ...

topics such as stochastic modelling, 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 back-testing methodologies. Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation ...