Vivek Kantamani

About

I am a statistician interested in tackling real-world problems through data science and machine learning projects, with a particular focus on developing data-driven statistical models and machine learning systems to facilitate operational and strategic risk-management decisions in business and quantitative finance.

Data Science and Machine Learning Experience:

• Effective at employing inferential statistics procedures to extract insights from data and support business decision-making.
• Proficient in constructing robust machine learning models for efficient monitoring of ML system metrics and KPI reporting.
• Experienced in facilitating data science and collaborative research projects using the Agile project management framework.

Private Equity Experience:

• Adept at developing inferential and predictive statistical models of valuation metrics (DCF, PEG, EV/EBITDA) and risk-management measures to facilitate private equity objectives through low-latency algorithmic implementations.

Quantitative Finance Experience:

• Effective at building quantitative financial models that derive excess returns from alpha-seeking systematic investment strategies and factor-based portfolio optimization methods while managing risk (VaR, ES) and ESG metrics.

Technical Skills:

Programming: Python (NumPy, Pandas, SciPy, Scikit-Learn, TensorFlow), R, Java, SQL
Databases/Tools: MySQL, SQLite, Git, Tableau, Microsoft Excel

Data Science: DSA, EDA, Data Mining, Pattern Recognition, Analytics Dashboard Development, KPI Reporting, A/B Testing
Machine Learning: Statistical Learning Theory, Linear Regression Analysis (GLMs), SVMs, DT/RF, Deep Learning (LSTMs)
Statistics: Statistical Inference (Bayesian), Causal Inference and Forecasting, Time Series Analysis, Stochastic Processes

Portfolio

Contact

• LinkedIn
• GitHub

Elements

i = 0;

while (!deck.isInOrder()) {
    print 'Iteration ' + i;
    deck.shuffle();
    i++;
}

print 'It took ' + i + ' iterations to sort the deck.';