About

Nitish is a skilled Data Scientist with over 3 years of experience in building Machine Learning Models, generating Data Driven Insights, and communicating results with stakeholders and customers. He has implemented the full life-cycle of Data Science projects from Data collection, Data cleaning, Data Analysis, Predictive modeling, and Optimization. Nitish has delivered projects in Python, SQL, and R and has implemented various algorithms such as Supervised Learning (Regression, Classification), Linear Models (Linear Regression, Logistic Regression, LASSO, Ridge, etc.), Ensemble Models (Gradient Boosting, Random Forest, Bagging, Boosting(Adaboost), etc.), Optimization (Interior point Method, Evolutionary algorithms (Differential Evolution, Particle Swarm, (mu + lambda) ES), and MultiObjective Optimization), and Hyperparameter Tuning (Using TPE, Particle Swarm Optimization). Nitish has also built Ensembles and implemented stacking of Models to improve Performance and has experience in Unsupervised Learning such as Clustering (KMeans, Hierarchical Clustering, DBSCAN, CURE); PCA, TSNE. In his current role as a Data Scientist at Ford Motor Company, Nitish is a part of the Business Sales Planning and Analytics Team (M&S) and has optimized Enterprise Profit by generating Quarterly Variable Marketing Strategies for Selected Vehicles in the UK and France. He has also forecasted Orders and distribution of customers across Financing Options along with Important KPIs for Business. In his previous role at TCS, Nitish was responsible for generating and presenting Analytics-based reports and building Machine Learning pipelines for Predictive Analytics and Prescriptive Analytics Use-cases. He has independently led the development of several Data Science, Machine Learning, and Analytics Use-cases. Nitish has reduced Power Plant losses (DFG) by 6% by building Machine Learning Models and performing Optimization, detected Customers with anomalous turbine operation by building Unsupervised Anomaly Detection systems, and designed a python package for automating hyper-parameter tuning for machine learning models using Optimization and for minimizing time complexity of machine learning pipeline. Nitish has a Btech in Electronics and Communication Engineering from the Institute of Engineering and Management, and he has completed his schooling from St. Xavier's School Durgapur and Sri Chaitanya, Visakhapatnam. Nitish is skilled in Data Science, Research Science, ML, SQL, Python, Supervised Learning, SVM, Random Forest, Hyperparameter, and has relevant experience of 6.62 years.