---
title: "Feature engineering | Skills We Assign For | Azendo"
description: "Feature engineering — domain knowledge over architecture, leakage, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/feature-engineering/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# Feature engineering.

Feature engineering transforms raw data into inputs a model can learn from: aggregations, encodings, ratios, time-based windows and domain-derived signals. It is usually where most of a model's performance comes from.

## Where Feature engineering fits on a long engagement.

On structured data, features beat architecture consistently. A well-chosen feature derived from domain understanding improves a model more than a more sophisticated algorithm applied to poor inputs, and teams reliably spend their effort the other way round.

Leakage is the failure that produces excellent offline scores and worthless production models. A feature computed using information not available at prediction time — a value updated after the outcome, an aggregate spanning the label period — teaches the model to cheat, and the score is impressive right up until deployment.

## What an assigned team does with Feature engineering.

The best features come from people who understand the business rather than from automated generation. Knowing that a particular sequence of customer actions precedes churn is domain knowledge, and no search over transformations discovers it reliably.

That is why access to your domain experts is treated as a delivery dependency rather than a convenience, as set out in [how an assignment runs](https://azendo.co/how-it-works/).

## What we use Feature engineering for.

* Signals derived from domain knowledge Features a business expert suggests, which automated search does not find.
* Leakage caught before deployment Point-in-time correctness verified, so offline scores are achievable.
* Effort spent where the return is Feature work prioritised over architecture on structured problems.

## How Feature engineering capacity is assigned.

Feature work is assigned inside AI capacity, with access to your domain experts treated as a delivery dependency.

## Roles we assign Feature engineering for

* [Machine Learning Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/machine-learning-engineer/)

## Service lines it sits in

* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

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

## Related in mlops and model delivery

* [MLOps handoff — skill we assign for](https://azendo.co/skills/mlops-handoff/)
* [ONNX — skill we assign for](https://azendo.co/skills/onnx/)
* [TensorRT — skill we assign for](https://azendo.co/skills/tensorrt/)
* [Model registry — skill we assign for](https://azendo.co/skills/model-registry/)
* [Weights & Biases — skill we assign for](https://azendo.co/skills/weights-and-biases/)
* [Comet — skill we assign for](https://azendo.co/skills/comet/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)

## Tell us what your roadmap needs Feature engineering 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/)