Rajath Rajendra Pai

Data Scientist

Hello! I’m currently working as an Associate Data Scientist at Volvo Group, where I build end-to-end solutions using the Microsoft Power Platform and Python. My focus is on predictive analytics and designing optimized, scalable, and reusable workflows.

In my free time, I participate in Kaggle competitions which I feel is a great way to keep learning, sharpen my skills, and contribute to the data science community. You can check out my Kaggle profile here. Outside of tech, I enjoy swimming, cycling. Feel free to reach out if you'd like to connect or chat!

Education

Master’s in Data Science and AI
Chalmers University of Technology, Sweden (2022 – 2024)
Bachelor of Engineering in Computer Science Engineering
NMAM Institute of Technology, India (2018 – 2022)

Experience

Volvo Group - Associate Data Scientist (March 2025 – Present)
- Optimized Power Platform solutions to streamline and automate internal business processes.
- Developed and maintained a role approval system that enables managers to initiate position requests with structured VP/SVP-level approval workflows.
- Built Python-based flows for automated certificate generation and developed an interface for distributing promotional certificates to managers.
- Designed and implemented a workforce analytics dashboard using Python, delivering insights into turnover, hiring trends, and gender distribution to support predictive analysis and strategic forecasting.
- Created a Power App and dashboard to help managers assess team flexibility, supporting planning and resource allocation across the organization.
Volvo Group - People Data Analyst (Sep 2024 – Nov 2024)
- Optimized ETL pipelines to enhance data processing efficiency.
- Automated data workflows using Python to streamline operations.
- Enhanced Power BI dashboards by optimizing PowerQuery and DAX queries for better performance.
Volvo Group - Summer Workshop – Production Logistics (Jul 2024 – Aug 2024)
- Processed and analyzed features to improve data matching accuracy for production logistics datasets using PySpark in Azure Databricks.
- Applied fuzzy matching and machine learning algorithms to identify optimal matching criteria.
- Collaborated in an agile framework with cross-functional teams to iteratively develop and refine solutions.
Volvo Group - Summer Intern - People Analytics Team (Jun 2024 – Jul 2024)
- Enhanced Power BI dashboards by optimizing PowerQuery and DAX queries for better performance.
Volvo Group - Master's Thesis Worker (Jan 2024 – Jun 2024)
- Developed a predictive model using multivariate time series data from truck sensors, external weather, and road-type datasets.
- Aimed to anticipate driver deactivation of ADAS functions to improve system design and usability.
Volvo Group - Summer Intern – People Analytics Team (Jun 2023 – Aug 2023)
- Designed and refined regression models for estimating human resource factors within the company.
- Enhanced Power BI reports to improve data visualization and accessibility.
Chalmers University of Technology - Research Assistant (Feb 2023 – Jun 2023)
- Conducted data analysis for the e-SAFER project at the Division of Vehicle Safety.
- Worked on analyzing data collected from e-scooters to identify patterns and safety improvements.

Core Technologies

Python
SQL
PyTorch
Databricks
PowerBI
PowerApps
PowerAutomate
MLflow
Git
Other Python Libraries

Featured Projects

RAG: Build it from Scratch
Built a RAG from scratch using Llama3 and deepseek-r1.
Automatic Medical Report Generation from Radiology Chest X-Ray using Deep Learning
Developed a predictive model using CNN (CheXNET for feature extraction), LSTM, and RNN to analyze frontal and lateral chest X-ray images and generate medical reports through text processing.
Scattering Parameters Parameterization
Leveraged TensorFlow and Keras to implement neural networks (Basic, LSTM, and GRU) for predicting input parameters in a human head-antenna system based on scattering parameters. The GRU model outperformed others, demonstrating high accuracy.
Face Anti-Spoofing
Designed a face anti-spoofing model using Histogram of Oriented Gradients (HOG) to extract features from images. Implemented a Support Vector Machine (SVM) classifier to accurately determine whether a face is real or fake.
AI Learns to Play Flappy Bird
Trained an agent to play a Flappy Bird clone using neuroevolution of augmenting topologies. Both the game environment and AI were built entirely in Python.