Sidharth Ramachandran
Senior Director, Data & AI
Nuremberg, Bavaria, Germany
Summary
👤
Senior🎓
Top SchoolSidharth Ramachandran is a data and AI leader with nine years of experience building production-grade analytics and ML products, currently Senior Director, Data & AI at RTL Deutschland in Nuremberg. He combines strategic leadership with hands-on expertise in text analytics and NLP—co-authoring an O'Reilly book and contributing practical Jupyter notebooks that demonstrate real-world text classification workflows. His background spans fintech fraud detection, credit-scoring analytics, and founding a startup CTO role, giving him a rare mix of product, domain and platform experience. As a GenAI trainer and consultant he translates emerging LLM tooling into business workflows and productivity gains across industries. Known for turning complex data problems into deployable solutions, he often bridges research, engineering and commercial teams to deliver measurable impact.
10 years of coding experience
14 years of employment as a software developer
Indian Institute of Technology RoorkeeMBA Strategy and Marketing, MBA Strategy and Marketing at Indian Institute of Management, KozhikodeIndian School Muscat
Stackoverflow
11reputation
228reached
0answers
1question
Github Skills (11)
scikit-learn10
data-analysis10
pandas10
jupyter-notebook10
machine-learning10
python10
text-classification10
natural-language-processing10
scikit10
matplotlib9
keras6
Programming languages (5)
Github contributions (5)
blueprints-for-text-analytics-python/blueprints-text
Oct 2020 - Sep 2022
Jupyter notebooks for our O'Reilly book "Blueprints for Text Analysis Using Python"
Role in this project:
Data Scientist
Data ScientistContributions:47 commits, 1 PR, 44 pushes in 1 year 11 months
Contributions summary:Sidharth's primary contribution is the addition of Jupyter notebooks detailing the implementation of text classification techniques using Python. The notebooks cover data preparation, train-test splitting, model training with LinearSVC, model evaluation (accuracy, confusion matrix, precision, recall), and a baseline comparison. The code leverages libraries such as scikit-learn, pandas and matplotlib for data manipulation, model building, and evaluation, demonstrating a focus on applying machine learning to text data.
jupyter-notebookpythontext-miningnatural-language-processingmachine-learning
sidhusmart/pyconde2018
Oct 2018 - Oct 2018
Contributions:14 commits, 11 pushes, 1 branch in 7 days
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