13th December 2025
9th May 2025
Research Assistant-Machine Learning & GenAI
Leeds School of Business, CU Boulder
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Machine Learning & Bayesian Modeling: Designed and deployed scalable Bayesian hierarchical models using Distributed MCMC, improving consumer segmentation accuracy by 25% and reducing inference computation time by 30% through advanced ML techniques like Gaussian Processes, SVMs, and Neural Networks.
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Finance & Data-Driven Marketing: Applied Bayesian computation with STAN for consumer preference analysis, optimizing marketing resource allocation and enhancing financial decision-making through probabilistic modeling on large-scale datasets (500K+ entries).
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Data Visualization & Business Impact: Built interactive dashboards (Tableau, Plotly) for real-time consumer insights, reducing marketing analysis turnaround time by 40%, improving executive decision-making with accessible, data-driven tools.
2nd September 2024 -
29th November 2024
Data Analysis and Web Development Intern
DreamState LLC
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Front-End Development & UI Optimization: Built interactive web components using React and Storybook, enhancing user experience and marketing effectiveness through consistent and scalable UI design.
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Data Processing & ETL Optimization: Managed ETL pipelines to process financial and marketing data, improving reporting efficiency and enabling real-time insights for strategic decision-making.
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Automation & Workflow Efficiency: Developed Python automation scripts to streamline data analysis and ML workflows, reducing manual tasks by 30% and increasing operational efficiency.
24th June 2024 -
23th August 2024
Data Analyst Intern
American Institute for Behavioral Research and Technology (AIBRT)
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Advanced Data Analysis & Machine Learning – Led data collection and applied ML models in Python and R to analyze large behavioral datasets, improving predictive accuracy by 20% and contributing to research publications.
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Financial & Marketing Analytics – Optimized analytical workflows, increasing project efficiency by 15%, and provided strategic recommendations for experimental design, influencing financial decision-making and marketing strategies.
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Data Visualization & Insights – Developed Power BI dashboards and presented key findings to stakeholders, translating complex data into actionable insights that shaped policy and business strategies.
1st November 2021 -
14th April 2022
Team Lead (Data Science)
Project Deep Blue
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AI-Powered Resume Parsing & HR Automation: Developed a machine learning-driven resume parsing system using Python, Flask, and MySQL, automating candidate data extraction from 500+ resumes daily. Improved HR efficiency by 40% through NLP-based categorization and structured data processing.
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Financial Data Optimization & Scalable APIs: Designed RESTful APIs for seamless front-end and back-end integration, optimizing financial data pipelines with caching and asynchronous processing, reducing system response time by 20%.
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ML-Driven Job Recommendation & Market Insights: Built an NLP-based job recommendation engine, enhancing candidate-job matching accuracy through feature engineering and model tuning. Optimized database queries for real-time market analysis, ensuring efficient data retrieval and business scalability.
17th April 2022 -
30st April 2023
Data Scientist
Converge IT Solutions
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Machine Learning & Data-Driven Insights: Developed AI-powered applications using AWS, Python, and ReactJS, optimizing workflows by 15%. Built predictive models for user behavior analysis, enhancing operational efficiency.
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Financial & Business Intelligence: Designed interactive Power BI dashboards for real-time financial monitoring, driving data-driven strategies that increased profit margins by 20%.
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Marketing Analytics & IoT Integration: Led the Audi Q7 pod project, leveraging motion sensing and data analytics to enhance user engagement. Applied ML techniques for consumer insights, earning CEO recognition for high-impact results.
1st August 2020 -
31th March 2021
Machine Learning Engineer
Association of Computing Machinery - ACM
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Predictive Modeling & Machine Learning: Developed a heart disease prediction system using Logistic Regression, improving early diagnosis accuracy by 25%. Applied advanced data preprocessing, feature engineering, and model evaluation techniques (AUC-ROC, cross-validation) to optimize performance.
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Financial & Risk Analysis: Integrated the model into hospital ERP systems, enabling real-time patient risk assessment and data-driven decision-making. Designed automated data pipelines for accurate financial forecasting and resource allocation in healthcare operations.
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Marketing & Business Insights: Collaborated with stakeholders to interpret machine learning outputs, transforming complex data into actionable insights. Optimized model performance through hyperparameter tuning, reducing false positives and enhancing predictive accuracy for targeted healthcare marketing strategies.
1st January 2020 -
31st July 2021
IoT Security Intern
IT Department - SAKEC
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IoT Security & Machine Learning: Led research on securing IoT devices using AI-driven threat detection, encryption, and real-time anomaly detection, improving system resilience by 25%. Applied machine learning for intrusion detection and risk assessment in connected ecosystems.
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Financial Risk Mitigation: Implemented zero-trust architectures and cryptographic protocols to safeguard IoT transactions and cloud integrations. Conducted penetration testing and vulnerability assessments, reducing financial fraud risks in IoT-enabled fintech applications.
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Marketing & Data-Driven Security Strategies: Collaborated with cross-functional teams to design scalable IoT security models, influencing security policies and compliance. Developed data-driven strategies for securing IoT-based customer analytics and marketing platforms, ensuring secure data transmission and user authentication.
1st June 2021 -
31th July 2021
Data Science Intern - (Workshop)
Shah and Anchor Kutchhi Engineering College
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Machine Learning & Statistical Analysis – Applied data mining, hypothesis testing, regression, and clustering to uncover key insights from large datasets, supporting predictive modeling and decision-making.
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Financial Data Processing & Optimization – Cleaned, preprocessed, and analyzed financial datasets, handling missing values and anomalies using Python (pandas, NumPy). Automated workflows to improve data integrity and efficiency.
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Marketing Analytics & Dashboarding – Developed interactive dashboards to monitor real-time trends, enhancing market strategy formulation and improving decision-making efficiency by 15% for stakeholders.