Ajay Arunachalam
Senior Data Scientist
Cambridge, England, United Kingdom
Summary
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Senior🎓
Top SchoolAjay Arunachalam is a Senior Data Scientist based in Cambridge with around nine years of experience building and productionising analytics and AI solutions, particularly on AWS. He combines academic rigor—a PhD in computer science—with hands-on industry work across telecommunications, research labs, and publishing, translating complex business and scientific problems into practical, scalable pipelines. His toolset spans deep learning, computer vision, time-series anomaly detection, symbolic regression, and real-time ML deployments, and he has led end-to-end automation and data engineering for petabyte-scale projects. Certified in AWS Solutions Architecture, AWS ML, Power BI, and Scrum, he pairs technical breadth with delivery discipline and mentoring experience. An interesting thread through his career is a focus on interpretable and semi-supervised methods—from AI-assisted data labeling platforms to explainable anomaly predictors—bridging cutting-edge research with production impact.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at National Institute of Development Administration (NIDA) ThailandAnna University, ChennaiB.E., Computer Engineering, B.E., Computer Engineering at Savitribai Phule Pune University
Github Skills (55)
forecasting-models10
classification10
machine-code10
regression10
clustering10
pycaret9
anomaly-detection9
shap9
python9
datasets9
machine-learning9
time-series9
matching9
gpu9
factor9
Programming languages (5)
Github contributions (5)
ajayarunachalam/msda
Feb 2021 - Oct 2021
Contributions:52 commits, 49 pushes, 1 branch in 8 months
ajayarunachalam/Deep_XF
Oct 2021 - Dec 2022
Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
Contributions:22 commits, 12 pushes, 2 comments in 1 year 1 month
forecastingsignalpythontime-series-analysissensors
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