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Data Science with less than a year in AI & Analytics
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Assessing your cultural and operational fit
Data Science postgraduate passionate about combining Analytics, Generative AI, and Machine Learning to transform data into strategic business decisions. Experienced in developing AI-powered solutions, predictive models, and insight-driven dashboards that bridge the gap between data and business outcomes.
Symbiosis Institute of Geoinformatics
M.Sc. · Data Science & Spatial Analytics
August 1, 2024 – June 30, 2026
Modern College of Arts, Science and Commerce
B.Sc. · Computer Science
August 1, 2019 – June 30, 2022
CodSoft
Data Science Intern
November 1, 2023 – December 1, 2023
India
Multi-Agent Financial Decision Support System using Agentic AI & Generative AI
June 19, 2026 – Present
• Developed a Multi-Agent AI system using LangChain/CrewAI to autonomously collect, analyze, and interpret real-time financial market data from multiple sources. • Built an AI-powered sentiment analysis engine leveraging NLP and financial news data to evaluate market sentiment and identify potential investment opportunities. • Designed and implemented a real-time risk assessment framework using Machine Learning models to analyze stock volatility, trends, and market behavior. • Integrated Generative AI (LLMs) to generate explainable investment recommendations (Buy/Sell/Hold) and automated financial insights in natural language. • Created an interactive Streamlit dashboard featuring live stock tracking, sentiment visualization, risk scoring, and AI-driven decision support for enhanced user experience.
Amazon Review Sentiment Analysis
June 19, 2026 – Present
• Analyzed 10,000+ customer reviews using Python, NLP, and Machine Learning techniques. • Generated sentiment-based business insights to identify customer satisfaction trends and product improvement opportunities. • Stored processed data in MongoDB and developed interactive Power BI dashboards for decision-making. • Applied AI-driven text analytics to convert unstructured customer feedback into actionable recommendations.
FinCoach - Explainable AI Framework for Loan Approval with Integrated Financial Coaching
June 19, 2026 – Present
• Developed a machine learning framework using XGBoost to predict loan approval outcomes on structured financial datasets. • Processed and analyzed 5,000+ financial records through data preprocessing and feature engineering. • Evaluated model performance using classification metrics such as accuracy, precision, recall, and confusion matrix. • Implemented Explainable AI (SHAP) to interpret predictions of the black box model and identify key financial factors influencing loan decisions. • Designed an interactive Streamlit based dashboard to present model predictions, explanation visualizations, and financial insights for improved decision transparency. • Integrated a financial coaching component to provide users with personalized recommendations for improving loan eligibility and financial planning.
Databricks Accredited Generative AI Fundamentals
Databricks Academy
March 1, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong interest in applying data science to real-world problems, particularly in finance and customer sentiment. The projects showcase initiative and a proactive approach to learning and implementing new technologies (e.g., CrewAI, LangChain, SHAP). The pursuit of a Master's degree and a Generative AI certification indicates a commitment to continuous learning and staying current with industry trends, which aligns well with a growth-oriented culture. The diversity of projects (financial decision support, sentiment analysis, loan approval) suggests adaptability and a broad interest in data science applications.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to translate complex technical concepts into actionable business insights, suggesting good problem-solving and analytical thinking skills. The focus on interactive dashboards and explainable AI points to an understanding of user experience and transparency, which are valuable for operational fit. However, without direct assessment data on communication or teamwork, these are inferred from project descriptions.