SUMMARY:

• Around 7 years of experience as a Professional Qualified Data Scientist/Data Analyst in Data Science and Analytics including Relational Databases, Machine Learning, Data Mining, and Statistical Analysis.
• Currently working with Confidential as a Data Science Consultant experienced in Database modeling and Data Mining with large datasets of Structured and Unstructured data, Machine Learning, and Data Visualization.
• Significant industry experience and domain knowledge in Healthcare, Financial Services and Gaming industries.
• Expertise in Python (2.x/3.x) programming with multiple packages including NumPy, Pandas, SciPy and Scikit - learn.
• Proven experience in Data Visualization tools like Seaborn, ggplot, Matplotlib, SSRS, Tableau and PowerBI.
• Strong business judgment and ability to take ambiguous problems and solve them in a structured, hypothesis-driven, and data-supported way.
• Hands on experience in implementing Machine Learning algorithms like K-Nearest Neighbors, Logistic Regression, Linear regression, Naive Bayes, Support Vector Machines, Decision Trees and Random Forests.
• Expertise in Machine learning Unsupervised algorithms such as K-Means, Density Based Clustering (DBSCAN), Hierarchical Clustering and good knowledge on Recommender Systems.
• Implemented various statistical tests like ANOVA, A/B testing, Z-Test, T-Test for various business cases.
• Experience in building models with Deep Learning frameworks like Tensor Flow and Keras.
• Actively involved in all phases of data science project life cycle including Data Extraction, Data Preprocessing, Data Modeling and Data Visualization.
• Involved in developing various ETL Packages to load and extract the data between flat files and heterogeneous database sources like Oracle, SQL Server, MySQL and Teradata.
• Experience with statistical programming languages such as R and Python.
• Expertise in leveraging the Exploratory Data Analysis with all numerical computations and by plotting all kind of relevant visualizations to do feature engineering.
• Good knowledge of Hadoop Architecture and various components such as HDFS, Job Tracker, Task Tracker, Name Node, Data Node, Secondary Name Node, MapReduce, PySpark concepts, and ecosystems including Hive and Pig.
• Experience in working on both windows, Linux platforms.

TECHNICAL SKILLS:

Languages: Python (Numpy, Pandas, TensorFlow, Seaborn, Matplotlib, NLTK, Sklearn), R, Spark, Kafka, SQL, RDMS, MangoDB, SSIS(ETL)

Machine Learning Algorithms: Classification, KNN, Regression, Random Forest, Clustering(K-means), NeuralNets, SVM, Bayesian Algorithm, Social Media Analytics, Sentimental analysis, Market Base Analysis, Bagging, Boosting.

Databases: SQL Server 2014/2012/2008/2005/2000 , MS-Access, Oracle 12c/11g/10g/9i

Tools: Microsoft Power BI, Tableau, SSIS, SSRS, SSAS, Informatica 6.1SQL Server Management Studio, SQL Server Enterprise Manager, SQL Server Profiler, Microsoft Office, Excel Power Pivot, Excel Data Explorer

PROFESSIONAL EXPERIENCE:

Confidential - RI

Data Science Consultant

Responsibilities:

• Involved in all phases of data acquisition, data collection, data cleaning, model development, model validation, and visualization to deliver data science solutions.
• Hands on experience with Python 3.6 (Pandas, NumPy, Matplotlib, SciPy, scikit-learn, seaborn) and Spark 2.0 (PySpark, MLlib) to develop variety of models and algorithms for analysis.
• Designed rich data visualizations to model data into human-readable form with Matplotlib.
• Experience in creating producer applications for sending files to Kafka and retrieving data from topics. Used Kafka to reduce the complexity of sharing data between IOS applications/systems.
• Exploration of various internal and external data sources, integration and preparation of data for consumption in advanced analytics as well as the execution of analytical tasks itself.
• Used many machine learning algorithms such as linear regression, classification, multivariate regression, Naive Bayes, Random Forests, K-means clustering, KNN for developing and implementing predictive models.
• Implemented clustering techniques like DBSCAN, K-means, K-means++ and Hierarchical clustering for customer profiling to design insurance plans according to their behavior pattern.
• Hands on experience in Dimensionality Reduction, Model selection and Model boosting methods using Principal Component Analysis (PCA), K-Fold Cross Validation and Gradient Tree Boosting.
• Executed machine learning models like Reinforcement learning, Decision trees, Random forest, clustering for retaining the churned customers.
• Worked on NLTK library in python for doing sentiment analysis on customer product reviews and other third-party websites using web scrapping.
• Used Python frameworks like TensorFlow and Keras for handling data and implementing neural networks for deep learning algorithms.
• Able to use snowflake connectors for processing, analyzing large data and enabling spark to read and write the data
• Performed Python coding for building time series forecasting and implemented visualizations for better understanding of forecasted data.
• Performed SQL queries on databases for data collection, augmentation and selecting the data variables in the process of analysis.
• Worked with numerous data visualization tools in python like matplotlib, seaborn, ggplot, pygal. Created and maintained reports to display the status and performance of deployed model and algorithm with Tableau.
• Created and implemented Data Wrangling scripts for each data science tasks to pull the data from different databases like Hadoop HBase, Oracle, SQL Server, PostgreSQL.
• Reviewed logical model with application developers, ETL team, DBAs, and testing team to provide information about data model and business requirements.
• Performed computational tasks on data by creating pig, hive and Map reduce scripts to access and transform data in HDFS.

Confidential

Data Scientist Consultant

Responsibilities:

• Worked with text mining problems using NLTK libraries based on the business requirements.
• Analyzed and significantly reduce customer churn using machine learning to streamline risk prediction and intervention models.
• We implemented and analyzed RNN based approaches for automatically predicting implicit relations in text. The disclosure relation has potential applications in NLP tasks like Text Parsing, Text Analytics, Text Summarization, Conversational systems.
• Worked with outlier analysis with various methods like Z-Score value analysis, Liner regression, Dbscan (Density Based Spatial Clustering of Applications with Noise) and Isolation forest
• Used cross-validation to test the models with different batches of data to optimize the models and prevent overfitting.
• Worked with PCA (Principle Component Analysis), LDA (Linear Discriminant Analysis) and other dimensionality reduction concepts on various classification problems on various linear models.
• Worked with sales forecast and campaign sales forecast models such as ARIMA, Vector Autoregression (VAR), Autoregressive Neural Networks (NNAR).
• Experimented with predictive models including Logistic Regression, Support Vector Machine (SVC) and re-enforcement learning to prevent the retail fraud.
• Created a customer service upgrade which is an automated chatbot to better assist the online customers using text classification and knowledgebase.
• Created a text classification model using RNN and LSTM with TensorFlow
• Developed classification models of user behavior based on website activity to create/enhance buying stage classifications.
• Developed BI dashboards using Tableau to represent different angle of data EDA.

Confidential

Data Scientist

Responsibilities:

• Developed machine learning, predictive analytics & Python modules for day to day business activities.
• Used pareto/NBD model to compute KVD at customer level, defined business context along 2-dimentions non-contractual vs. contractual and continuous vs. discrete.
• Worked with behavior tracking algorithms to predict customer yield and churn rate.
• Implemented various machine learning algorithms to analyze the customer stake amount and predicted gross win value for different categories.
• Developed and implemented Customer Churn models to assess the customer churn and provided business insights to retain the customer.
• Studied and implemented Unique Active Player models to monitor the Stake Amount from customer bases and alert them with updates.
• Implemented personalized customer recommendation systems for customer retention and attain different customer groups.
• Worked with statistical data using python financial services libraries like Numpy, scipy, pandas.

Confidential

Data Analyst

Responsibilities:

• Database management, maintenance and data analysis, processing and testing.
• Tested and ensured data accuracy through the creation and implementation of data integrity queries, debugging and trouble shooting.
• Worked with business analysts for understanding the problem statement and their requirements.
• Extracted data from various relational databases and performed SQL queries depending on how the data needs to be modified, Used FTP to download SAS formatted data.
• Developing new code and modifying existing code to extract data from various data sources like DB2, oracle, Used MS-excel and SQL server extensively to manipulate the data for business requirements.
• Worked on creating new datasets from raw data using some importing techniques and modifies the datasets which already existed using join, set, sort, merge, update and some other conditional statements.
• Issues either in informatica mappings and building data warehouses or the UNIX scripts were handled proactively.
• Closely worked with Machine learning engineers to analyze the data based upon their requirements. Experienced in creating pivot tables for analyzing data in excel.
• Hands-on experience in data analyzing and wrote MySQL queries for improved performance.
• Optimized queries with some manipulations and modifications in MySQL code and removed unwanted columns and duplicate data.
• Exceeded expectations by helping machine learning team in data cleaning and preprocessing which is used for building their machine learning algorithms.
• Involved in implementation of scheduling jobs using UNIX.