SUMMARY:

• Highly experienced professional with over 10 years’ experience, which includes 6 years’ Experience in Data science inData Extraction, Data Modeling, Data Wrangling, Statistical Modeling,Data Mining, Machine LearningandData Visualization & 4 Years in Software Engineering experience in testing of different areas of Software Development Life Cycle (SDLC).
• Domain knowledgeand experience inFinance and Healthcare industries
• 2+ years' experience in Agile background of software/Datadesign, development, deployment to build services and customer support in Enterprise applications using Object Oriented Analysis and Design (OOAD)
• Experience in all the Latest BI Tools Tableau, Power BI Dashboard Design and SAS
• Analyze and extract relevant information from large amounts ofDatato help automate for self - monitoring, self-diagnosing, self-correcting solutions and optimize key processes
• Strong experience withPython (2.x,3.x)to developanalytic modelsandsolutions.
• Strong experience with model building in R
• Strong analytical skills with experience in machine learning methods and statistics
• Proficient inPython 2.x/3.xwithSciPy Stackpackages includingNumPy,Pandas,SciPy,Matplotlib and Python.
• Expertise and experience in MSQL, Mysql Teradata, NoSQL databases
• Excellent knowledge in Normalization (1NF, 2NF, 3NF and BCNF) and De-normalization techniques for improved database performance in OLTP, OLAP andDataWarehouse/DataMart environments
• Expertise in Project Management, Analysis, Estimation, with a unique mix of managerial,functional, domain, technical and client handling skills
• Familiar with model building using neural networks.
• Familiar on building models with bigDataframeworks like Azure and Apache Spark

TECHNICAL SKILLS:

Data Science: Regressions, Hypothesis Testing, Time Series Analysis, Random Forests, Boosting, SVM, Nonlinear Methods, Neural Networks, ANOVA

Programming Languages: C#, SAS, VBA, Python (NumPy, SciPy, Pandas, Gensim, Keras), R (Caret, Weka, ggplot)

Statistical languages/tools: R, Shiny, Python, SAS, Tableau, PowerBI

Core Programming Skills: C#, Java, C++, MS SQL, VBA

QA: Unit test, integration testing, end to end testing and UAT

Other Skills: CFA Charter Holder, Requirements Gathering, Project Management

PROFESSIONAL EXPERIENCE:

Confidential, Chicago, IL

Data Analyst

Environment: Python, Tableau, PowerBI, R, SQL, Apache Spark

Responsibilities:

• Developed and deployed a time series-based model to predict revenue using current cross-sectional data and historical trends. Achieved an accuracy of greater than 90%
• Used advanced Microsoft Excel functions such as Pivot tables, power pivot and power query
• Worked on complex quries and procudures in Mysql and MSSQL
• Built models using Microsoft Azure Machine learning library
• Worked withdataframes and otherdatainterfaces in R for retrieving and storing thedata.
• Datawrangling to clean, transform and reshape thedatautilizing NumPy and Pandaslibrary.
• Responsible for retrievingdatausing SQL from the database and perform analysisenhancements.
• Used clustering technique K-means clustering to identify outliers and to classifyunlabeleddata.
• Performed various statistical tests in R and Python for clear understanding thedata& produced forecast trends for various categories.
• Gathereddatafrom multiple web and other sources, used SQL queries to join and aggregate variousdatasets.
• Applied various machine learning algorithms such as Logistic Regression, LinearRegression and Support Vector Machines (SVM) in Python.
• Prepared thedatato perform analyses by using Dimensionality Reduction techniquessuch as Principal Component Analysis (PCA) to reduce the number of features.
• Performeddataanalysis, statistical analysis and generated reports, listings and graphsusing R and Python.
• Analyzed, optimized and implemented a predictive Machine Learning model usingPython using Random Forest and K-Nearest Neighbors (KNN) for identifying profitable referral sources.
• Participated in developing quantitative models using statistical and machine learningalgorithms to describe market behavior.
• Addressed overfitting and underfitting by tuning the hyper parameter of the algorithmand by using L1 and L2 regularization.
• Developed a sales target model using boosting algorithms to identify potential hospital having high expected value
• Implemented multiple algorithms to estimate the duration of treatment for a population of patients which led to greater inventory projections and operational efficiency
• Worked on detailed visualizations, simulations and data analysis in multiple platforms such as tableau, power BI, R and Python

Confidential, Jacksonville, Fl

Data Analyst

Environment: C++, Python, SQL, Apache Spark

Responsibilities:

• Developed models in cross asset (FX, Equities and Bonds) using machine learning algorithms (SVM, Random Forests)
• Developed and maintained coss-assest risk-premia models using regressions techniques.
• Performed detailed model testing out of sample using cross validation
• Worked on ensemble machine learning methods using Apache Spark framework.
• Performeddatavisualization, generated graphs, charts and reports for the analysisresults.
• Use R and SQL to clean and manipulatedataby analyzing and eliminating duplicate, inaccurate and dirty data.
• Performeddatavisualization, generated graphs, charts and reports for the analysisresults.
• Apply and use appropriate classification and regression models to develop the predictive models using Random Forests, Decision Trees, Support Vector Machines(SVM) and K-means clustering.
• Performed QA and model validation on suite of quantitative models.
• Worked on a support vector machine (SVM) algorithm to create an entry and exit signal for the strategies
• Enhanced the existing strategies using co-relation analysis and QA’d as required.
• Developing several asset allocation techniques including min volatility, risk budgeting, volatility targeting

Confidential, Chicago, IL

Data Analyst

Environment: C#, SQL, Python, R

Responsibilities:

• Was a lead data analyst for the Transaction Cost Analysis (TCA) product
• Worked on providing quantitative analysis for execution quality, broker, venue & algorithm analysis using statistical and machine learning techniques
• Developed models to estimate impact of trades using statistical methods such as multiple linear regression and time series regression
• Improved model forecast using regularization and Principal component analysis (PCA)
• Used Python (NumPy, SciPy and Pandas) to work with large data sets of trading data
• Developed client report and visualizations using tableau