Clearwater Analytics is the leading SaaS platform for investment accounting, risk, and performance. We serve some of the world's largest insurance companies, hedge funds, asset managers, and institutional investors. We deliver decision-ready risk analytics that bring clarity and insight to multi-asset portfolios-highlighting exposures, sensitivities, scenarios, and performance drivers.



As a Senior Lead Quantitative Developer for Energy and Commodities you will own the commodity analytics on our platform and lead the London quant team. The product spans financial and physical commodity products, from linear instruments through to exotic payoffs, and the models and risk analytics behind them. You will work with developers across the platform, present models to clients and their validators, and take what comes back into the code. Half the job is building. The other half is running a team that builds.




What You'll Do

• Own the pricing libraries for financial and physical commodity products, including exotic payoffs


• Build and calibrate the models behind them, and the numerical machinery those models need


• Extend our multi-factor pricing framework so that adding a payoff is configuration rather than a new pricers


• Make it fast enough for production: compiled path generation, variance reduction, and performance tests that fail when a change


• makes things slower


• Build the analytics around the price - sensitivity testing, cash flow generation - across commodity portfolios


• Present models to clients and to model validators, and answer what comes back with evidence rather than assertion


• Lead the London quant team. Hire, run one-to-ones, and take responsibility for the growth of the people on it


• Set technical direction for commodities across the platform, and hold the standard on code review, testing, and model documentation

• 10+ years in quantitative development, with most of it in energy and commodities


• Experience leading developers, as a line manager or a tech lead who owned delivery for a team


• Multi-factor commodity models: construction, calibration, and the market conventions they have to respect


• Exotic payoffs, and experience pricing them in a framework rather than one pricer at a time


• Monte Carlo, including least-squares Monte Carlo for early exercise and path-dependent structures


• PDE methods, and the judgement to know when a problem wants a grid rather than paths


• Linear and dynamic programming for constrained optimization problems


• Production Python, plus performance work in a compiled language where Python is not enough


• The ability to explain a model to someone who does not share your background, and be understood

• Physical asset valuation and optimization: storage, transportation, generation, load


• Front office development on a commodities or energy desk, supporting pricing, hedging, and risk


• Exotic options outside commodities - FX, equity, convertibles - and the frameworks that price them together rather than separately


• Having built something other quants build on, and maintained it while they did