Jaime Huertas
Co-Founder Head Of AI at eComID
Stockholm, Sweden
Summary
👤
Senior🎓
Top SchoolJaime Huertas is a data-driven AI leader and entrepreneur with a decade of experience building production-grade recommendation systems and ML infrastructure across retail and industry. As Co-Founder and Head of AI at eComID and former Senior Machine Learning Engineer at H&M, he delivered the company's first neural and session-based recommenders and real-time model serving that scaled to millions of users. He has founded Shaped to help teams add personalization, led anomaly detection and recommender projects at Sandvik and Polystar, and contributed educational time-series forecasting work on GitHub that bridges research and practical implementation. Trained in computer science and machine learning at UPV and KTH, he blends academic rigor with hands-on engineering to move models from prototype to live systems. Based in Stockholm, Jaime combines product sensibility with deep ML expertise and a knack for turning research insights into measurable UX improvements.
11 years of coding experience
7 years of employment as a software developer
Computer science Machine learning, Computer science Machine learning at KTH Royal Institute of TechnologyUniversitat Politècnica de València
Spanish, English
Stackoverflow
736reputation
66kreached
16answers
61questions
Github Skills (12)
data-visualizations10
pandas10
data-visualisation10
data-visualization10
time-series-forecasting10
statsmodels10
python10
machine-learning9
cron6
pytorch6
deep-learning6
generative-adversarial-network6
Programming languages (18)
Github contributions (5)
jiwidi/time-series-forecasting-with-python
Nov 2019 - Sep 2022
A use-case focused tutorial for time series forecasting with python
Role in this project:
Data Scientist
Data ScientistContributions:78 commits, 9 PRs, 13 pushes in 2 years 10 months
Contributions summary:Jaime initiated the project by creating the main notebook for a time series forecasting tutorial, providing context from relevant research papers. The user also added code to install requirements and load, explore, and visualize the Beijing air pollution dataset. Furthermore, the user has worked to apply SES, HWES and ARIMA models and visualize the results.
pythontime-series-forecastingtime-seriestime-series-analysistime-series-prediction
jiwidi/DeepSpeech-pytorch
Jul 2020 - Jan 2021
Pytorch implementation for DeepSpeech 2.0
Contributions:40 commits, 8 PRs, 32 pushes in 6 months
pytorchlibrispeech-datasetasre2e-asrdeep-learning
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