Quant Software Engineer - Energy

The energy group prices, values and manages the risk of physical OTC derivatives in volatile energy markets. Unlike purely financial contracts, these positions carry physical supply chain constraints, storage capacity limits and nonlinear pricing, and the group's systems must model all three.

You will write the pricing and valuation models behind the group's risk and trade clearing, implementing the mathematics directly in code. You will also build the distributed systems that run these models at scale, splitting pricing and risk calculations across a compute grid and combining the results, working alongside quantitative researchers and traders. Correctness comes first: risk metrics must be calculated deterministically and must not fail during the largest market swings.

Target profile:

The main requirement is a track record of writing OTC derivative pricing or valuation models yourself, in production code. We are seeking a quant developer with a financial engineering background and 3 to 10 years of experience, who can take the mathematics of a model and implement it natively, weigh the trade-offs between computational approaches, and reason from first principles about risk and valuation. You should also know how to keep a distributed run reproducible and correct when machines fail or load spikes. A degree combining computer science and financial engineering is a strong fit. Commodities experience is not required, though you will stand out if you have priced storage, swing, spread or tolling deals, or used mean-reverting price models such as Schwartz-Smith.

Skills and expertise

• OTC derivative pricing models, linear and nonlinear

• Option pricing (e.g. Black-76, spread options, Asian options)

• Monte Carlo methods, including Least Squares Monte Carlo

• Trees and PDE / finite difference methods

• Valuation and cashflow models

• Distributed compute grids for pricing and risk (e.g. Ray, Dask, Spark or in-house grids)

• Reproducible parallel computation (random seeding, ordered aggregation, consistent market data snapshots)

• Fault tolerance (retries, idempotency, checkpointing)

• High-throughput backend systems and messaging (e.g. Kafka)

• Python (main language)

• C++