---
title: "QuantLib | Skills We Assign For | Azendo"
description: "QuantLib for derivatives pricing — curves, conventions and calibration, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/quantlib/"
---

[Skills](https://azendo.co/skills/) Analytics and BI 

# QuantLib.

QuantLib is an open-source library for quantitative finance, providing instrument pricing, yield curve construction, volatility models, date and calendar handling, and Monte Carlo and finite-difference engines.

## Where QuantLib fits on a long engagement.

The unglamorous parts are what make QuantLib valuable. Day count conventions, business day adjustments, holiday calendars per market and settlement rules are tedious, error-prone and completely unforgiving — a wrong convention produces a valuation that is subtly and persistently incorrect. Having them implemented and tested is worth more than the pricing engines.

Curve construction is where most valuation error actually originates. Instrument selection, interpolation method and bootstrapping choices change discount factors and therefore every valuation that depends on them. Two teams with the same market data and different curve configurations get different numbers, both defensible.

## What an assigned team does with QuantLib.

Financial libraries need testing against known benchmarks rather than against themselves. Validating pricing against published values or an independent implementation is the only way to establish that a configuration is correct.

That validation discipline is part of the delivery standard for quantitative work, agreed as part of [how the monthly fee is built](https://azendo.co/pricing/).

## What we use QuantLib for.

* Conventions handled correctly Day counts, calendars and settlement rules implemented and tested rather than approximated.
* Curves built deliberately Instrument selection and interpolation chosen explicitly, because they drive every valuation.
* Pricing validated against benchmarks Results checked against published values rather than trusted because they run.

## How QuantLib capacity is assigned.

Quantitative development capacity is assigned under [data engineering outsourcing](https://azendo.co/services/data-engineering/), with benchmark validation part of the definition of done.

## Roles we assign QuantLib for

* [Quantitative Analyst Data science](https://azendo.co/services/data-science/quantitative-analyst/)

## Service lines it sits in

* [Data science](https://azendo.co/services/data-science/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in analytics and bi

* [Tableau — skill we assign for](https://azendo.co/skills/tableau/)
* [semantic layers — skill we assign for](https://azendo.co/skills/semantic-layers/)
* [A/B testing — skill we assign for](https://azendo.co/skills/a-b-testing/)
* [experimentation design — skill we assign for](https://azendo.co/skills/experimentation-design/)
* [causal inference — skill we assign for](https://azendo.co/skills/causal-inference/)
* [cohort analysis — skill we assign for](https://azendo.co/skills/cohort-analysis/)
* [segmentation — skill we assign for](https://azendo.co/skills/segmentation/)
* [metric definition — skill we assign for](https://azendo.co/skills/metric-definition/)

## Tell us what your roadmap needs QuantLib for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)