Avinash Navlani - Technical Architect

Avinash Navlani

Technical Architect - Data Science

Indore, Madhya Pradesh, India

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Summary

👤

Senior

🎓

Top School

Avinash Navlani is a Technical Architect in Data Science with nine years of experience building and operationalizing ML solutions across cloud and on-prem environments, from feature engineering to deployment and monitoring. He pairs advanced analytics and Operations Research—using tools like Gurobi—to solve routing, scheduling, and pricing problems, and has productionized price recommendation and tanker routing systems. Comfortable across the stack, he combines MLOps, data engineering (PySpark, Airflow, Databricks) and software engineering best practices (APIs, Docker, Kubernetes, CI/CD) to scale AI. He also develops domain-specific NLP solutions including custom NER for healthcare and contributes practical ML examples to public repositories (e.g., PCA and classifier implementations in a Packt Python Data Analysis book repo). Based in Indore, he brings an academic foundation in Data Science and OR plus teaching experience, which helps him translate complex research into business-ready products.

8 years of coding experience

11 years of employment as a software developer

Rajiv Gandhi Proudyogiki Vishwavidyalaya

Master of Technology Data Science and Operations Research, Master of Technology Data Science and Operations Research at Devi Ahilya Vishwavidyalaya

English, Hindi

Stackoverflow

Stats

495reputation

169kreached

12answers

6questions

Github Skills (14)

scikit10

scikit-learn10

pandas10

machine-learning10

pca10

python10

classification10

numpy10

data-analysis10

apache-spark6

goodness-of-fit6

wordcount6

grid-search6

counting6

Programming languages (1)

Jupyter Notebook

Github contributions (5)

PacktPublishing/Python-Data-Analysis-Third-Edition

Sep 2020 - Jan 2021

Python Data Analysis, Third Edition, Published by Packt

Role in this project:

Data Scientist

Contributions:29 commits, 30 pushes in 3 months

Contributions summary:Avinash's contributions focus on implementing Principal Component Analysis (PCA) and applying various classification algorithms on the provided dataset. The user has written code to create and fit PCA models using NumPy and scikit-learn, including sorting and selecting principal components. Further contributions include implementing and evaluating Naive Bayes, Decision Tree, K-Nearest Neighbors, and Support Vector Machine (SVM) classification models, demonstrating a strong emphasis on model training and evaluation metrics. The user also explored the Elbow Method to determine the optimal number of clusters.

avinashnavlani/Free-Data-Science-Classes

Feb 2025 - Mar 2025

Contributions:16 pushes, 1 branch in 1 month

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