Responsibilities : 1. Responsible in developing system models, prediction algorithms, solutions to prescriptive analytics problems, data mining techniques, and/or econometric model. 2. Communicate the results with operations team for taking best decisions and Collect data needs and requirements by Interacting with the other departments. 3. Demonstrated and build statistical/machine learning systems to solve large-scale customer-focused problems and leveraging statistical methods and applying them to real - world business problems 4. Perform Data Profiling to learn about behaviour with various features of turnover before the hiring decision, when one has no on-the-job behavioural data. 5. Performed preliminary data analysis using descriptive statistics and handled anomalies such as removing duplicates and imputing missing values. 6. Application of various machine learning algorithms and statistical Modelling like decision trees, text analytics, natural language processing NLP,supervised and unsupervised, regression models, social network analysis, neural networks, deep learning, SVM, clustering to identify Volume using Scikit-learn package in python 7.Performed data cleaning and feature selection using PCA, TF-IDF Vectorization. 8.Understanding the business problems and analysing the data by using appropriate Statistical models to generate insights. 9.Developed NLP models for Topic Extraction, Sentiment Analysis Identify and assess available machine learning and statistical analysis libraries including regressors, classifiers, statistical tests, and clustering algorithms . 10.Work with NLTK library to NLP data processing and finding the patterns. Categorize comments into different using Text Analytics. Environment : Python 2.x, R, SQL Server 2012, Microsoft Excel,

Responsibilities : 1.Analysing large data sets apply machine learning techniques and develop predictive models, statistical models and developing and enhancing statistical models by leveraging best-in-class modelling techniques. 2,Well experienced in Normalization and De-Normalization techniques for optimum performance in relational and dimensional database environments. 3.Understanding requirements, the significance of weld point data, energy efficiency using large datasets 4.The model was developed to identify risk undertaken by each customer using machine algorithms. This analysis Helps Company to tighten the lending process by identifying risky and default investment. Also the analysis involves the various criteria in order to point out the most probable bench mark in the sector of customer rating, product analysis, quality etc. Environment : Python 2.x/3.x, Scikit-Learn/SciPy/NumPy/Pandas/Matplotlib/Seaborn, Machine Learning algorithms Random Forest, Gradient Boosting tree, Feature Engineering, Feature Section . .

Responsibilities : 1.Collaborated with data engineers and operation team to implement the ETL process, wrote and optimized SQL queries to perform data extraction to fit the analytical requirements. 2.Wrangled data, worked on large datasets acquired data and cleaned the data, analysed trends by making visualizations using python 3.Used Python to develop a variety of models and algorithms for analytic purposes. 4.Conducted analysis and patterns on customers' shopping habits in a different location, different categories and different months by using time series Modelling techniques. 5.Used RMSE/MSE to evaluate different models' performance. 6.Designed rich data visualizations to model data into human-readable form with Matplotlib. Environment : Python, SQL, Matplotlib