• 🎓 B.Tech + M.Tech Dual Degree in Bioengineering — IIT Mandi (2020–2025) | CGPA: 7.55
• 💼 ~1 year of experience across Data Analytics, Salesforce Development, and Business Operations
• 🔍 Passionate about turning raw data into actionable business insights
• 📊 Built KPI dashboards tracking 331+ stores at a healthcare startup using Power BI
• 🤖 Trained deep learning models (YOLOv8) on 1,000+ microscopy images for my M.Tech thesis
• 🌐 Proficient in Japanese (JLPT) | English | Hindi
• 🎯 Currently seeking Data Analyst / Business Analyst roles in India

• Built 10+ Lightning Web Components (LWC) using Apex & JavaScript to streamline business workflows
• Automated processes using Flows, Triggers & Approval Processes — reduced manual effort by 40%
• Managed structured CRM data supporting loan management workflows and internal reporting

• Supported student acquisition by counselling prospective learners and driving lead conversions across assigned cohorts
• Tracked batch-level operational metrics to identify drop-off trends and recommend retention-focused interventions
• Coordinated with academic and marketing teams to streamline enrollment workflows and meet business growth targets

• Performed data wrangling & EDA on multi-company pharma sales datasets using Excel
• Conducted KPI analysis across 331+ stores to identify top revenue-driving products
• Built interactive Power BI dashboards for performance tracking, trend analysis & stakeholder reporting
• Applied descriptive statistics & comparative analysis to uncover store-level demand patterns

YOLOv8 · ImageJ · Deep Learning · Microscopy Analysis

• Object detection pipeline on 1,000+ high-res microscopy images to automate stomata identification
• Implemented image augmentation, dataset annotation & morphological feature extraction
• End-to-end pipeline: data preprocessing → training → benchmarking → error analysis

SVM · XGBoost · Random Forest · MetaboAnalyst

• Identified 4 key disease biomarkers from metabolomics data with 92% accuracy
• Full ML pipeline: preprocessing → feature selection → model training → evaluation

Streamlit · Scikit-learn · NLTK · TMDb API

• Content-based recommender using Cosine Similarity & CountVectorizer on 5K+ titles
• NLP preprocessing with PorterStemmer; deployed live via Streamlit

Time Series · ARIMA · Python

• Statistical forecasting model for financial time series data
• Applied stationarity checks, differencing, and ACF/PACF analysis

Machine Learning · Classification · EDA

• Predicted sleep disorders from real-world health data (BMI, stress, activity levels)
• Compared multiple classifiers with cross-validation

Arduino · Python · Ultrasonic Sensors

• Wearable assistive device with vibration/audio feedback
• Achieved < 0.5s response time; optimized 3D-printed casing

"Bridging biology, business, and data — one model at a time."