I am a passionate and aspiring Master’s student in Data Science at the State University of New York at Buffalo, driven by curiosity and creativity and blending technical expertise with a deep sense of purpose. With a foundation in machine learning, big data systems, and AI-driven product development, I thrive at the intersection of innovation and impact. My work bridges the gap between technical depth and meaningful impact — whether it’s designing predictive models, automating data workflows, or crafting digital wellness solutions powered by AI.
My professional journey began in HR analytics, where I developed machine learning models to improve employee retention and built robust ETL pipelines using PySpark and SQL. I believe in creating human-centered tools — evident in projects like Detox Dial, an AI-powered wellness app.
With hands-on experience in Python, SQL, R, Tableau, Power BI, and cloud platforms like AWS, I thrive on challenges that blend data engineering, ML, and storytelling.
Want to know more about my professional experience and education? Check out my resume below!
Data Analyst, Nucleus Business Solutions (May 2023 – Dec 2023)
Led development of machine learning pipelines using Scikit-learn to predict employee attrition and workforce demand, boosting HR forecasting accuracy by 20%. Built scalable ETL pipelines in PySpark and SQL to automate ingestion and aggregation of structured HR data (ATS, payroll, reviews). Developed Power BI and Tableau dashboards to track turnover trends, satisfaction, and recruiting funnel metrics in real-time. Collaborated with HR, engineering, and leadership to translate insights into actionable workforce strategies.
Data Analyst Intern, Nucleus Business Solutions (Oct 2022 – Apr 2023)
Conducted deep-dive analysis of hiring and retention data using Python (Pandas, NumPy) and SQL to uncover trends in candidate conversion and early-stage attrition. Designed interactive dashboards in Power BI to visualize source-of-hire effectiveness, pipeline velocity, and cost-per-hire metrics. Supported predictive modeling efforts by performing data cleaning, feature engineering, and exploratory data analysis (EDA). Delivered insights that shaped sourcing allocation strategies and improved stakeholder reporting clarity.
Developed a multi-modal AI system integrating GPT-chat, AI calls, and behavior analytics to tackle digital addiction. Integrated UI/UX design principles and built a user-centered intervention system with measurable outcomes. Leveraged data to enhance digital wellbeing.
View Project
Designed and implemented a relational database system for 5,000+ laptops using PostgreSQL to enable efficient querying, filtering, and analysis of device specifications for data-driven insights.
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Implemented unsupervised learning using K-Means and time-series preprocessing to uncover crypto market segmentation. Utilized Matplotlib and Seaborn for comparative volatility visualization and pattern clustering validation.
View ProjectPython (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Web Scraping), R, SQL (Advanced), VBA for Excel, Bash, PowerShell
MySQL, PostgreSQL, Snowflake, Amazon Redshift
Regression, Classification, Clustering, Decision Trees, Neural Networks, Time Series Analysis, Scikit-learn, TensorFlow
Pandas, NumPy, SQLAlchemy
Power BI (DAX, RLS), Tableau, Amazon QuickSight, Streamlit, Shiny (R), Excel (Pivot Tables, Charting, Macros)
GitHub, Jupyter Notebook, MATLAB, MS PowerPoint, AWS (IAM, Data Governance)