I am a Data Scientist with +4 years of experience developing actionable BI solutions, implementing up-to-the-minute data analytics, deploying models into production, and helping fulfill business & stakeholder needs with data-driven insights. Throughout my career, I have applied big data analytics across multiple industries by drawing on my expertise in various data science fields, including artificial intelligence, machine learning, predictive modeling and forecasting, and market and sentiment analyses. In my current role, I analyze and forecast trends in the energy markets by sourcing custom data sets and ensuring data access to cross-functional teams. Prior to this, I constructed a pipeline integrating different data sources to generate key insights for Northeastern University. Besides data analytics, my leadership, communication, and project management skills allowed me to oversee, mentor, and collaborate with multifunctional teams. I delivered a special project for India's leading telecommunication service provider in half the budgeted time by effectively hiring, training, and supervising 17 engineers and 120 field executives. My Selected Accomplishments include: - Developed a data science job portal using data scraped from Glassdoor, performed EDA & ETL on the data science job market, and tuned a supervised recommendation system based on user behavior using classification models. - Facilitated 2 initial product investments worth $180K by presenting KPIs on ROI optimization and customer demand. - Increased data pipeline efficiency by initiating the use of Agile best practices; automated and deployed data analysis model in Azure. - Formulated the first data governance model for data acquisition and access based on organizational hierarchy My skillsets include: - Python & Packages: Numpy, Pandas, Seaborn, Matplotlib, SciPy, Scikit Learn, TensorFlow - R: caret, tidyverse, dplyr, ggplot2, shiny - Machine Learning Techniques: Linear and Logistic Regression,k-NN and k-Means Clustering, Classification, Decision Trees, Random Forest, Bagging, Boosting, XgBoost, PCA, LDA, Naive Bayes. - Deep Learning: C-NN, R-NN, OpenCV, NLP - Data Science Techniques: Recommendation Systems, Time-Series Analysis, Market Segmentation, A/B Testing - Tableau and PowerBI - Database: MySQL, Postgre SQL, Hadoop, Hive, Spark - Cloud: AWS S3, SageMaker, Azure Databricks If you’re looking for someone who executes advanced analytics and statistical modeling projects by solving complex data-related issues and leading multi-practice teams, email me: anishnitin6@gmail.com.