---
title: "SciPy | Skills We Assign For | Azendo"
description: "SciPy for scientific computing — optimisation, signal processing, and the Azendo roles assigned for it."
url: "https://azendo.co/skills/scipy/"
---

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

# SciPy.

SciPy is the scientific computing library for Python, providing optimisation, integration, interpolation, signal processing, linear algebra and statistical distributions on top of NumPy arrays.

## Where SciPy fits on a long engagement.

SciPy is where the numerical methods live that most applied work eventually needs. Constrained optimisation for scheduling or allocation, interpolation over irregular measurements, signal filtering over sensor data, and a comprehensive set of statistical distributions — all implemented by people who understand the numerics.

The implementations are also more careful than a hand-rolled equivalent. Numerical stability, convergence behaviour and edge-case handling in these routines represent a great deal of accumulated expertise, and reimplementing them is a reliable way to introduce subtle errors that appear only on unusual inputs.

## What an assigned team does with SciPy.

Optimisation problems in particular need someone who understands the solver rather than only the API. Convergence failures, local minima and badly scaled problems all present as "the answer looks wrong" and are diagnosed by understanding what the method is doing.

That depth is developed deliberately rather than picked up, which is the purpose of the [Talent Success and Academy](https://azendo.co/talent-success-academy/) programme.

## What we use SciPy for.

* Constrained optimisation for allocation Scheduling and resource problems solved with a method suited to the constraints.
* Signal processing over sensor data Filtering and spectral analysis using implementations that handle the numerics properly.
* Distributions handled correctly Statistical functions used where a hand-rolled version would be subtly wrong.

## How SciPy capacity is assigned.

Scientific computing capacity is assigned inside data science work under [data engineering outsourcing](https://azendo.co/services/data-engineering/).

## Roles we assign SciPy 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 SciPy 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/)