Nigel Grech
Squad Lead at Husqvarna Group
Jönköping, Sweden
Summary
👤
Senior🎓
Top SchoolNigel Grech is a data scientist and engineering leader based in Jönköping, Sweden, with a decade of experience spanning data science, software engineering, cloud development, automation and lecturing. As Squad Lead at Husqvarna Group (after progressing from Data Scientist), he guides cross-functional teams to turn complex data problems into production-ready solutions. He combines hands-on modeling and deep learning skills (Udacity Nanodegree) with practical software delivery experience from consulting and early-career development roles. Known for mentoring and building diverse teams, he balances technical depth with a focus on scalable, automated pipelines that drive business impact. An analytical thinker with roots in AI from L-Università ta' Malta, he brings both academic grounding and real-world execution to data-driven product development.
10 years of coding experience
1 year of employment as a software developer
Nanodegree Deep Learning, Deep Learning, Nanodegree Deep Learning, Deep Learning at UdacityBachelor’s Degree, Bachelor of Science in Information Tech (Hons) Artificial Intelligence, B, Bachelor’s Degree, Bachelor of Science in Information Tech (Hons) Artificial Intelligence, B at L-Università ta' Malta
Swedish, Maltese, English
Github Skills (22)
ddd8
mhealth8
distributed-computing8
data-collection8
classification-model5
infrastructure5
hadoop5
big-data5
distributed-systems4
image-analysis3
machine-learning2
artificial-intelligence2
python2
algorithms2
julia2
Programming languages (3)
Github contributions (5)
NGrech/NGrech
Mar 2021 - Oct 2025
Contributions:1 push, 1 branch in 4 years 7 months
NGrech/plant-leaf-diseases-identification
Sep 2021 - Dec 2021
In this repo we tackle the problem of plant leaf disease identification based on the PlantVillage data set. This work is part of the AI for Data Science course in the Applied Data Science MSc @ Malmo University. Our aim it to hand build a number of algorithms which we will use to train classification models, and compare them to existing solutions provided by leading libraries.
Contributions:31 PRs, 35 pushes, 19 branches in 2 months
aiclassification-modeldata-science
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