Monte Carlo simulation specialists for your dedicated team.

Where Monte Carlo simulation fits in a long-term product team.

Simulation is the right tool when the question is about a distribution. Project completion dates, capital at risk, capacity under uncertain demand and insurance exposure are all cases where the tail matters more than the centre, and a single expected value actively misleads.

The output is only as good as the input distributions, and those are usually the weakest part. Assuming normality where the data has heavy tails, or independence between inputs that move together, produces a distribution that is precise and wrong, and the precision makes it more persuasive than it should be.

What your dedicated team does with Monte Carlo simulation.

Dependence between inputs is where most simulations go wrong. Modelling correlated risks as independent understates tail risk severely, which is exactly the region the simulation was built to examine.

Getting that right is specialist statistical work, handled by a dedicated data science team alongside the engineering that takes it to production.

Common Monte Carlo simulation use cases.

• Ranges for planning decisions A distribution of completion dates or costs, in place of a single figure few believe.
• Tail risk quantified Extreme outcomes estimated where the average is not the decision-relevant number.
• Dependence modelled carefully Correlated inputs handled as such, because independence understates the tail.

Adding Monte Carlo simulation skills to your team.

Simulation work sits with data science, with input distributions documented as assumptions.

Monte Carlo simulation specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.