Rajath Pai's Mugshot

Rajath Rajendra Pai

Aspiring Data Scientist | Chalmers University of Technology

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)

Technical Skills

  • Programming Languages:
    Python, C++, Java, C, SQL, R, Matlab
  • Data Manipulation and Analysis:
    Pandas, NumPy, Scikit-learn, PySpark
  • Machine Learning and Deep Learning Frameworks:
    TensorFlow, PyTorch, Hugging Face, LangChain
  • Big Data and Distributed Computing:
    Databricks
  • Data Visualization and Reporting:
    Power BI, Matplotlib, Seaborn, Streamlit
  • MLOps and Model Deployment:
    MLflow
  • Databases:
    Microsoft SQL Server, PostgreSQL
  • Version Control:
    Git
  • Soft Skills:
    Storytelling with Data, Effective Communication, Cross-Team Collaboration

Experience

  • 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.

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.
  • Health First App
    Created an Android app that provides detailed information about food items, their health benefits, and the diseases they can help prevent.

Competitive Programming

  • Google Codejam 2021: Global Rank 1117 in Round 1C; qualified for Round 2.
  • Google Kickstart 2021: Global Rank 302 in Round H.
  • Codechef August 2021 Long Challenge: Global Rank 30.
  • Codechef July 2021 Long Challenge: Global Rank 37.

Workshops, Training, and Certification

  • Machine Learning with Google DSC
    Participated in a workshop on Machine Learning, organized by NMAM Institute of Technology in collaboration with Google DSC.
  • Algorithms and Data Structures
    Earned a certification from edX, in association with Microsoft.
  • CS229: Machine Learning
    Completed the certified course offered by Stanford University.

Strengths

  • Lifelong Learner (Philomath)
  • Effective Team Player
  • Strong Communication Skills (Articulate)
  • Reliable and Consistent

Hobbies

  • Photography
  • Drawing