Vraj Nena

Dynamic and result driven individual seeking data graduate role. or internship opportunities. Showed accelerated working through hands-on projects and assessments along with collaborating with team members to attain and publish valuable insights from data. Led different assessment which includes streamlining data collection and analysis process alongside organizing and consolidated large data set using Apache spark and different cloud storage platforms to obtain meaningful patterns.

• Databricks Data Lake Fundamentals
• SQL
• Azure AI Fundamentals
• Google Cloud
• Big Data Engineering (ongoing)

Vital Voices, (Ongoing) 

• Led a university team in developing a data-driven project to analyse mental health trends and suicide ideation, with a focus on men's mental health due to gambling.
• Implemented Agile methodologies, serving as the team lead and adopting a democratic leadership approach to foster collaboration and innovation.
• Designed and implemented a robust data collection pipeline, leveraging multiple open-source platforms and APIs., Developed CRON jobs for automated, scheduled data retrieval to ensure consistent and up-to-date datasets.
• Architected and deployed cloud infrastructure on Azure, utilizing a B1 VM with SQL Server for relational database management within the free tier.
• Implemented comprehensive security measures, including configuring Azure Virtual Network ingress and egress rules, and conducted multiple security tests to ensure data protection.
• Implemented Redis with Celery workers to manage asynchronous tasks and improve system performance for data processing jobs.
• Established CI/CD pipelines using Azure DevOps and GitHub Actions, implementing industry best practices for efficient and reliable deployments.
• Technologies Used -Python, SQL, Azure Cloud Services (VM, SQL Server, Virtual Network), API Integration, CRON, Azure DevOps, GitHub Actions

Object Detection on Kubernetes Platform (Completed)

• Developed a scalable platform for object detection using advanced machine learning algorithms.
• Deployed machine learning models for image detection on a Kubernetes cluster, demonstrating proficiency in containerization and orchestration technologies.
• Gained hands-on experience with Oracle Cloud, setting up a Kubernetes cluster with 1 master and 2 worker nodes.
• Created a React application, containerized it using Docker, and deployed it on the Kubernetes cluster.
• Implemented load testing using Locust, demonstrating the application's ability to handle 10 concurrent users efficiently.
• Technologies Used - Kubernetes, Docker, Oracle Cloud, React, Python, Machine Learning (Object Detection algorithms), Locust (Load Testing)

Traffic Congestion Analysis (Completed)

• Analysed traffic patterns and identified congestion hotspots across Melbourne using data from VicRoads API.
• Developed a real-time dashboard using Tableau to visualize ongoing traffic disruptions on a map of Melbourne.
• Created a comprehensive historical data analysis dashboard to identify trends and patterns in traffic congestion.
• Performed data cleaning and preprocessing using Python to handle large-scale traffic datasets.
• Utilized Azure cloud storage for efficient data management and scalability., Conducted statistical analysis to identify factors contributing to traffic congestion and predict high-risk areas.
• Technologies Used - Python (Pandas, NumPy), SQL, Tableau, Azure Cloud Storage, Geospatial Analysis, Data Visualisation, API Integration