Hey! I'm Janhavi 👋 Welcome to my GitHub profile where I share projects, innovations, and ideas at the intersection of technology and management. I'm a CAPM®-certified Professional and a Former Analyst with a passion for designing intuitive, data-driven solutions that align with both user needs and business goals. With experience at Deloitte USI and a deep understanding of Salesforce, project management, and strategic innovation, I’m currently pursuing my Master’s in Management of Technology at ASU and actively exploring full-time opportunities!

🎓 MS in Management of Technology, Arizona State University (Expected Dec 2025)

🏢 Former Analyst at Deloitte USI | Interned as Salesforce Developer at ForceArk Consulting

📍 Based in: Tempe, Arizona | Originally from Pune, India

🌱 Currently expanding my knowledge in advanced project management, UX design, and AI-powered business solutions.

💬 Ask me about: Salesforce, product design, project management frameworks, and transforming business problems into tech solutions.

⚡ Fun fact: I’ve authored five research papers—from 🤖 AI-powered military systems to smart agriculture—and I’m always looking for ways to turn research into real-world impact.

Reimagined Snapchat UX with Quiet Mode, real-time location sharing, and a customizable Discover feed. Created high-fidelity prototypes and user journeys for intuitive navigation.

Planned and executed a full project lifecycle for a social media scheduling app using WBS, Gantt charts, and risk mitigation strategies—bridging technical execution with business vision.

Used frameworks like Porter’s Five Forces and VRIO to analyze growth opportunities, market entry barriers, and innovation scope in the smart home industry.

Captured and analyzed live Twitter data using Python and Hive, visualized with Power BI to assess public sentiment on a health crisis. Demonstrated how companies, governments, and healthcare providers can make informed, real-time decisions during public health emergencies using big data pipelines.

Analyzed global mental health survey responses using DASS-21. Built 7 ML models including SVM, Random Forest, AdaBoost, and Voting Classifiers—achieving 100% accuracy for classifying depression, anxiety, and stress severity levels.

Developed a real-time gesture recognition system to convert military hand signals into speech. Achieved 99% accuracy using CNN, Haar cascades, and MediaPipe. Designed for battlefield communication—lightweight, high-speed, and highly accurate, even under resource constraints.