* Experienced and self-motivated data scientist with experience in Statistical Modeling, Data Mining, Machine Learning, and Data Visualization. * Profound knowledge and hands on experience in implementing supervised machine learning algorithms, such as K-Nearest Neighbors, Logistic Regression, Linear regression, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. * Proficient in unsupervised machine learning algorithms, such as K-Means, Density Based Clustering (DBSCAN), and Hierarchical Clustering. * Hands on experience in implementing Dimensionality Reduction Techniques, such as Principal Component Analysis and Linear Discriminant. * Profound knowledge and hands on experience on implementing Univariate and Multivariate Time Series Analysis, such as Moving Average, Autoregressive, Exponential Smoothing (ES), Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA), Vector Autoregressive (VAR), and Vector Error Correction (VEC). * Excellent experience on Python programming language, and packages, such as Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and SciPy to apply data cleaning, data manipulation, data mining, machine learning, and data visualization. * Good knowledge on Deep Learning concepts like Multi-Layer Perceptron, Deep Neural Networks, and Artificial Neural Networks. * Experienced in SQL for ETL (Extract, Transform and Load). * Proficient in data visualization tools, such as Tableau 10.5, Python Matplotlib/Seaborn, R ggplot2 to generate charts like Box Plot, Scatter Chart, Pie Chart and Histogram etc., and to create visually impactful and actionable interactive reports and dashboards. * Familiar with Big Data Analytics tools, such as Hadoop MapReduce and Spark (PySpark) ecosystems. * Actively involved in all phases of data science project life cycle including Data Collection, Data Pre-Processing, Exploratory Data Analysis (EDA), Feature Engineering, and Feature Selection. * Performed various inferential statistics, such as Statistical Hypothesis Testing on mean and standard deviation (z-test, t-test, Chi-square test, f-test, and ANOVA) * Experienced in several feature selection methods, such as Mutual Information, Correlation Analysis, and F-test. * Employed Confusion matrix, Recall, Precision, and AUC and Precision-Recall curve to check the performance of classification models. * Having experience in using collaborative tools like GitHub. * Strong analytical skills to transform business resources and tasks into regularized data and analytical models, designing algorithms to support clients in various environments for solution development phase.
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