P

PRANAL KADAM

Junior · Engineering | Data

Machine learning graduate with hands-on experience delivering production-ready analytics, risk models, and deep learning systems across structured data, computer vision, and NLP.

Junior Resume Verified GitHub Connected Social Verified Data Analytics & Decision Science: BCG X, Deloitte (Forage)

Profile updated about 2 months ago

Most Recent Role

Technical Trainer

Dextero

Mar 2023 – Sep 2023

Education

Master of Science in Machine Learning

University of Arizona

Skills & Technologies

10 detected

Languages

Python
Python
SSQL

Other

MMachine Learning
DDeep Learning
NNLP
DData Analysis
DDatabases
CCloud
VVisualization
LLibraries

Certifications

1

Data Analytics & Decision Science: BCG X, Deloitte (Forage)

Work Experience

D

Technical Trainer

Mar 2023 – Sep 2023

Dextero

  • Led hands-on training for 4 working professionals in Python, SQL, and data analytics.
  • Delivered RPA-driven, business-aligned analytics in an early-stage startup under rapidly changing requirements.
S

Software Developer

Jul 2022 – Dec 2022

Shivaami Cloud Services

  • Developed 4+ AppSheet applications and Google Apps Script automations including Nykaa to streamline business workflows.
  • Created Looker Studio dashboards to enable secure, data-driven internal operational reporting.

Education

2025 – 2025

University of Arizona

Master of Science in Machine Learning

2021 – 2021

DRK Institute of Science & Technology

Bachelor of Technology in Computer Science & Engineering

10

Skills

2

Experiences

GitHub Activity

 
View @pranall on GitHub

Social Profiles

LinkedIn
LinkedIn
GitHub
GitHub
LeetCode
LeetCode

Projects

5 built

Production Data Cleaning & Integrity Engineering

  • Normalized 56K+ housing records using SQL-only pipelines, standardizing 100% date fields, imputing 1K+ missing addresses, and eliminating duplicates to deliver an analytics-ready schema supporting pricing and reporting.
SQL

Global COVID-19 Analytics & Vaccination Progress Pipeline

  • Built an end-to-end pipeline over multi million row datasets across 200+ countries, validating 100% of public records and computing mortality, infection, and vaccination metrics for reliable cross-country analysis and decision support.
SQL

Few-Shot Face Verification System using Siamese Neural Networks

  • Implemented a Siamese network with triplet loss, achieving 96–100% accuracy (5–25 way) using 128-D L2-normalized CNN embeddings, enabling scalable identity verification without retraining.
Deep Learning

YOLO Object Detection

  • Trained YOLOv5n/s/m on 627 images / 1,194 annotations, reaching 0.616 mAP@0.5 at ~44 ms inference.
Computer Vision

Credit Risk Modeling and Loan Default Prediction

  • Evaluated 8 models on 1,000 German loan records under class imbalance, selecting Gradient Boosting (PR-AUC ≈ 0.62).
Machine Learning

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