I’m building expertise in statistics, experimentation, data analytics, AI, product thinking, marketing, and software engineering through hands-on projects and structured learning.

My academic background combines Computer Science Engineering, Finance, and Data Science, and my professional experience spans banking, asset management (mutual funds and hedge funds), data science, and technology, where I’ve developed technical and product-focused analytical thinking along with client-facing and business-facing expertise.

I’m particularly interested in building and analyzing AI and machine learning-driven research and trading systems, portfolio management tools, scalable data platforms, innovative products, and data-driven marketing and financial solutions.

• AI / LLM Skills: OpenAI API, Claude API, Gemini CLI
• LLM Foundations, MCP
• RAG architecture with Vector Databases

Jupyter Notebooks, Google Colab, Docker, Git

• Advanced SQL and data science
• Experimentation and causal inference
• Product analytics
• Data visualization and dashboards
• AI tools, ML engineering, and LLM workflows

Statistics & Experimentation

• Regression (Linear, Logistic) - Foundational Statistical Modeling
• Hypothesis Testing - Statistical Reasoning
• Statistical Analysis - Descriptive and Inferential Analytics
• A/B Testing & Experimentation - Applied Experimental Design
• Causal Inference - Connecting Experiments/Observational Data to cause-and-effect
• Machine Learning Applications - Applied Predictive Modeling

Data Analytics

• SQL
• Python (Pandas, NumPy)
• exploratory data analysis

Data Visualization

• Seaborn
• Plotly
• Streamlit dashboards

Software & Technical Skills

• Python
• Git and GitHub
• data analysis workflows

Applications

• product analytics
• marketing analytics
• financial data analysis

Python implementations and exercises for algorithmic problem solving.

https://github.com/macrodatascience/algorithms-and-data-structures

Data analysis and visualization projects using Python, Pandas, NumPy, Plotly, Seaborn, and Streamlit.

https://github.com/macrodatascience/data-analysis-projects

Machine Learning Projects implemented, and lab work for Foundations of Modern Machine Learning Program.

https://github.com/macrodatascience/ML

https://github.com/macrodatascience/fmml2021

Practice problems and solutions from multiple platforms demonstrating SQL concepts and analytical querying.

https://github.com/macrodatascience/sql-portfolio

Organized notes and code snippets from courses, tutorials, and experiments in data analytics, algorithms, and AI.

https://github.com/macrodatascience/learning-notes

• Advanced SQL and data analysis
• Experimentation and causal inference
• Product analytics
• Data visualization and dashboards
• AI tools and workflows

• Python
• SQL
• R
• Jupyter Notebooks
• Streamlit
• Git & GitHub
• PostgreSQL, MySQL, Snowflake, Clickhouse, KDB-X