Skills

Senior Data Scientist, Tiger Analytics (Jan’25 - Present)

  • Revenue Optimizer
    • Methods
      • Optimized revenue generation by applying the Method of Moving Asymptotes (MMA) to operate within defined constraints.
    • Tools
      • R: Utilized the nloptr package, a powerful tool for nonlinear optimization.
  • Forecasting Time Series variable
    • Methods
      • AutoRegressive (AR) and Vector AutoRegressive (VAR) Models.
    • Tools
      • Python (pandas, numpy, statsmodels)

Data Scientist, Tiger Analytics (Aug’23 - Dec’24)

  • Price Optimization through Market Analysis
    • Methods
      • Analyzed market performance using historical Key Performance Indicators (KPIs) to generate a composite score.
      • Forecasted future KPIs using Time Series Models.
    • Tools
      • Python (pandas, numpy) for comprehensive data analysis and manipulation.
      • Excel Solver for optimization of weighting factors.
  • Representative Property Identification for Market
    • Methods
      • Performed K-Means clustering analysis on property and location attributes to group similar properties.
      • Applied Local Outlier Factor (LOF) technique to identify and remove outliers within each cluster.
      • Calculated cluster centroids and used cosine similarity to identify representative properties for each market.
    • Tools
      • R (data.table, Rlof) for data analysis.
      • R (leaflet, mapview) for interactive geospatial visualization and map generation.
  • Finding Optimal Leases for Discussion
    • Methods
      • Employed Fedorev’s Exchange Algorithm to generate optimal hypothetical lease scenarios.
      • Utilized cosine similarity to select the most representative and relevant optimal leases.
    • Tools
      • R: AlgDesign package for implementing the Federov’s Exchange Algorithm.
      • Excel: Developed a macro to automate the generation of an Excel dashboard from tabular data, facilitating clear and concise presentation of results.

Senior Analyst, Tiger Analytics (Jul’21 - Jul’23)

  • Clustering of Patients
    • Methods
      • Polytomous Variable Latent Class Analysis (poLCA) for patient group identification.
      • Decision Tree Classification (rpart) for new patient classification.
    • Tools
      • R (poLCA, rpart, openxlsx)
      • Excel Dashboard for user-friendly results visualization.
  • Warehouse Rent Prediction for different Win Probability
    • Methods
      • Linear Regression, Gradient Boosting Machines (GBM), XGBoost for predicting Rent.
      • Logistic Regression (for win probability)
    • Tools
      • R (dplyr, gbm, xgboost, ggplot2)
      • Dataiku

Analyst, Tiger Analytics (Jan’21 - Jun’21)

  • Statistical Test of Significance
    • Methods
      • Statistical t-Test, z-Test, and proportion Test.
    • Tools
      • dplyr, tidyverse, spss packages in R.

Academics

  • Statistical Simulation (Bayesian Estimation, MCMC)
    • Tools
      • R Programming
      • \LaTeX for report generation
  • Computation of Descriptive Statistics for Practical
    • Tools
      • C Programming, Excel
      • R Markdown for Report Generation.