Jul 2021 – Aug 2023 · 2 yrs 1 mo · India · Remote

• At Fractal, I played a dual role across Data Science and Backend Engineering, contributing to high-impact solutions for global clients in finance and transportation domains. I worked closely with cross-functional teams to build, scale, and optimize intelligent systems, ranging from NLP chatbots to large-scale scheduling microservices.
• Key Achievements:
• 🧾 Customer Insights & Credit Risk Models: Designed and deployed machine learning models (Random Forest, SVM, Decision Trees) for a major financial institution, improving customer rating accuracy by 30% and supporting accurate loan/credit approvals.
• 💬 NLP Chatbot Development: Built a multilingual chatbot for automating customer support workflows, reducing response time by 50% and increasing satisfaction scores by 30%.
• ⚙️ Real-time Scheduling Microservices: Engineered high-throughput microservices handling 3M+ requests/hour for 56M+ users, leveraging Go, RabbitMQ, and MySQL to optimize backend scalability and reliability.
• 🔁 Cloud Migration & CI/CD: Migrated 20+ legacy schedulers to AWS, implemented CI/CD pipelines, and delivered a 100% ROI in the first year via cost optimization and automation.
• 📊 Data Infrastructure Optimization: Enhanced predictive analytics pipelines on Azure, improving processing speed by 40% and reducing query latency by 25% through advanced SQL and pipeline tuning.
• 🔄 ETL Workflow Development: Built scalable data pipelines using Azure Data Factory and Python, doubling data throughput and enabling reliable data integration across distributed platforms.
• Tools & Technologies:
• Python, Go, Azure, SQL, TensorFlow, SpaCy, RabbitMQ, MySQL, Azure Data Factory, CI/CD, Git, NLP, Machine Learning, ETL Pipelines, Microservices, REST APIs

Jan 2021 – Apr 2021 · 3 mos · Hyderabad, Telangana, India

• At SumTotal Systems, I worked as a Data Science Intern focused on applying deep learning techniques for security and behavioral analysis in video footage. My contributions centered around enhancing detection accuracy and accelerating threat recognition in real-time systems.
• Key Achievements:
• 🔍 Aggressive Behavior Detection: Researched and fine-tuned deep learning models to detect aggressive behavior from video data, resulting in a 5–13% improvement in classification accuracy across multiple datasets.
• 🎯 Threat Detection Using R-CNN: Developed and integrated Faster R-CNN models for handgun detection in surveillance footage, significantly improving threat detection performance and response times.
• ⚙️ Model Optimization & Evaluation: Conducted experiments with CNN architectures, fine-tuned hyperparameters, and deployed lightweight models for faster inference in resource-constrained environments.
• Tools & Technologies:
• Python, TensorFlow, Faster R-CNN, OpenCV, CentOS, Deep Learning, Computer Vision, Video Analytics

Jun 2019 – Aug 2019 · 2 mos · Greater Hyderabad Area

• At TESYNC Technologies, I focused on modernizing data infrastructure and business intelligence capabilities across multiple manufacturing plants. My work involved building scalable pipelines and interactive dashboards to support real-time decision-making at scale.
• Key Achievements:
• 🔄 Data Pipeline Design: Built automated pipelines to transform raw JSON and flat files into normalized fact/dimension tables, ensuring 3NF compliance and maintaining data integrity across 30M+ rows.
• 📊 Manufacturing BI Dashboards: Designed and optimized 30+ Power BI dashboards, driving adoption among 200+ users and transitioning the organization from static to interactive reporting.
• ⚙️ Workflow Automation: Streamlined reporting workflows for 10+ plants, reducing manual overhead and enabling real-time insights into manpower allocation, job progress, and critical purchase requests (PRs).
• Tools & Technologies:
• SQL, Power BI, Excel, Python (for data prep), Data Modeling, ETL, Business Intelligence, Reporting Automation