Data Python
Resume:
Nishanth
************@*****.***
PROFESSIONAL SUMMARY
Over 5+ years of work experience as a Data Scientist, including deep expertise and experience with Statistical Analysis, Data Mining and Machine Learning Skills using Python.
Experience in transforming business requirements into analytical models, designing algorithms, building models,
developing Data mining and reporting solutions that scales across massive volume of structured and unstructured Data.
Experience in Data Migrations, Data Cleaning, Transformation, Integration, Data Imports and Data Exports.
Proficient in Machine Learning/Deep Learning, Data/Text Mining, Statistical Analysis and Predictive Modeling.
Efficient in data acquisition, storage, analysis, integration, predictive modeling, logistic regression, decision trees, data mining methods, forecasting, factor analysis, cluster analysis and other advanced statistical techniques.
Skilled in machine learning algorithms: support vector machine, decision tree, random forest, artificial neural network, boosting, K-NN classification and K-means clustering algorithm and Naive Bayesian classifier.
Strong background in deep learning algorithms: CNN, inception network, VGG, ResNet, RNN and LSTM.
Experience in analytics, working with data to convert large volumes of structured and unstructured data into actionable insights and business values.
Experience in analyzing raw data, drawing conclusions and developing recommendations.
Hands on experience in implementing Naive Bayes and skilled in Random Forests, Decision Trees, Linear and Logistic Regression, SVM, Clustering, neural networks and Principle Component Analysis.
Good experience working with large datasets and Deep Learning algorithms using Keras and Tensor Flow.
Strong programming skills in Python, familiar with various python libraries such as Pandas, NumPy, seaborn, SciPy, MatPlotLib and Scikit-Learn.
Experience in database design using SQL to write Queries, Stored Procedures, Functions and Views.
EDUCATION
Master’s in Electrical Engineering – 2017
Bachelor’s in Electrical and Electronics Engineering – 2014
TECHNICAL SKILLS
Data Science: Classification, Regression, Prediction, Dimensionally Reduction, Machine Learning,
Hypothesis Testing, Deep Learning, Neural Networks
Programming: Python, R, SQL, Pyspark, Java, C++, Matlab/Simulink, AutoCAD
Scripting Libraries: Numpy, Pandas, Scikit-Learn, NLTK, SciPy, Tensorflow, Keras, OpenCV
Big Data: Google Cloud Platform, AWS, Hadoop/Hive,
Database: Oracle, SQL Server, MySQL, DB2, Sybase, PostgreSQL
Operating Systems: Linux/Unix
PROFESSIONAL WORK EXPERIENCE
Comcast Corporation, Philadelphia, PA Sep 2018 – Till Date
Data Scientist / Machine Learning
Corporation is a global telecommunications conglomerate headquartered in Philadelphia, PA. The project is under the Technology, Product, Xperience (TPX) team, and is focusing on supporting innovative products and services that bring internet and TV to residential and business customers.
Responsibilities:
Extracted, cleaned and manipulated machine log data from MySQL, AWS S3 and Splunk.
Generated dashboards to monitor Xifinity’s entertainment operating system X1’s performances, and to make sure the products are reliably available, using Tableau.
Developed matrix to measure customer experiences and detected buggy devices for the engineers to investigate before it reaches a certain point that customers must call for help.
Performed clustering analyses to segment customers into different groups based on their service experiences and product engagement using Python and PySpark.
Implemented algorithms such as Principal Component Analysis (PCA) and t-Stochastics Neighborhood Embedding (t-SNE) for dimensionality reduction and normalize the large datasets.
Performed K-means clustering, Regression and Decision Trees and performed Data Visualization reports for the management using R.
Implemented classification algorithms such as Logistic Regression, K-NN neighbors and Random Forests to predict the Customer churn and Customer interface.
Analyzed questionnaire questions using Named Entity Recognition (NER) Natural Language Processing (NLP) to classify degree of bad connections to the customers.
Performed Sentimental analysis on the email feedback of the customers to determine the emotional tone behind the series of words and gain the express of the attitudes and emotions by Long-Short Term Memory (LSTM) cells in Recurrent Neural Networks (RNN).
Performed classification models to search for the most impactful features to our KPIs.
Collaborated with the product team to test newly developed product features.
Visualized analysis results using python Plotly and Tableau dashboards and presented findings to executive management on a weekly basis.
Environment: Python 3.x, MySQL5.5, Spark 2.1, Splunk, AWS S3, Databricks
ELKEM INC, Cliffwood, NJ May 2017 – Aug 2018
Data Scientist / Machine Learning
Project was to build models for forecasting marketing sales and revenues by applying machine learning methods, principle component analysis, and regression on large dataset.
Responsibilities:
Collaborated with Data Engineering team in automating the data preparation and data transformation for model consumption.
Conducted extensive research on revenue management and pricing analytics.
Understood the business problem and identified the key challengers.
Applied various machine learning techniques such as Gradient Boosting, Neural Network and Random Forest to predict marketing outcomes (e.g. sales and revenues), achieving about 10% more accurate prediction of performance than previous years.
Used PCA to reduce the dimensionality of the data to accelerate the training process.
Deployed feature engineering techniques to generate new features and select features with Scikit-learn library in Python.
Used cross-validation to train the models with different batches of data to optimize the models and prevent over fitting.
Evaluated performance of the model through different methods, such as Accuracy, Precision, Recall and F1 score.
Reported actionable, statistical and analytical insights to executives for effective strategic positioning in marketplace.
Analyzed feedbacks from employees regarding Microsoft applications usage in their day to day tasks and built predictive models using machine learning algorithms to understand the main issues those are hindering usage of these apps by the employees.
Documented results obtained and supplied Digital fluency reports of each individual team to their respective team leads.
Environment: Python (Numpy, Pandas, Scikit-learn), Confusion matrix, Classification report, Gradient boosting, Neural Network, Random Forest, Principal Component Analysis, Predictive modelling
Reserve Bank of India – India Jun 2015 – Jan 2016
Summer Intern
Responsibilities:
Developed in-depth working model and application of credit derivatives over the globe
Compared the available Indian market instruments with Credit derivatives, OTC derivatives.
Analyzed whether Indian market needs any credit derivative instrument
Developed model for rural credit risk monitoring and mitigation
Aadya Trading & Investment (Pvt.) Ltd – India Aug 2014 – May 2015
Data Scientist/Quant Analyst
Responsibilities:
Led business side in development of proprietary trading software for securities.
Participated in preparation of Business Requirement Document, Functional requirement document, UAT.
Communicated and coordinated with other departments to gather business requirements.
Gathering all the data that is required from multiple data sources and creating datasets that will be used in analysis.
Performed Exploratory Data Analysis and Data Visualizations using R, and Python.
In Preprocessing phase, used Pandas and Scikit-Learn to remove or impute missing values, detect outliers, scale features, and applied feature selection (filtering) to eliminate irrelevant features.
Conducted Exploratory Data Analysis using Python Matplotlib and Seaborn to identify underlying patterns and correlation between features.
Devised long term trading strategies based on analysis of macroeconomic factors of different markets and sectors.
Devised short- and medium-term trading strategies based on technical analysis of stocks and indices
Correctly forecasted the easing of euro crisis based on political and economic factors and generated 25% ROI
Correctly forecasted the alliance of top two aviation firms of Indian market and generated 22% ROI
Performed valuation, fundamental analysis, balance sheet analysis, cash flow analysis of firms
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