Data Analyst

Teja Jami

Data Scientist/Machine Learning Engineer

Phone: +1-551-***-**** E-mail: *****.**@*****.*** LinkedIn: https://www.linkedin.com/in/teja-j-ml/

Professional Summary:

Over 5+ years of experience in Machine Learning, Data Analytics and Data mining with large Data Sets of Structured and Unstructured Data, Data Acquisition, Data Validation, Predictive modeling, Data Visualization, Web Crawling, Web Scraping, Statistical Modeling, Data Mining and Natural Language Processing (NLP), Multivariate Analysis, model testing, problem analysis, model comparison, and validation.

Experience working with machine learning supervised algorithms – Linear Regression, Logistic Regression, Linear Discriminant Analysis (LDA), Decision Tree, Random Forest, Support Vector Machines (SVM), Naïve Bayes, K – Nearest Neighbor. Also, un-supervised algorithms – Hierarchical clustering, K-means clustering, Probability Clustering, Density-Based Clustering (DBSCAN).

Hands on analytics Models like Decision Trees, Linear & Logistic Regression, R, Python, MS Excel, SQL, and MySQL.

Experienced in developing deep-learning models like Artificial Neural Networks – Multilayer Perceptron’s (MLPs), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) using TensorFlow for pattern recognition, recommendation systems, prediction analysis, machine translation, social network filtering image & video recognition.

Well versed using Dimensionality Reduction Techniques like Principal component analysis (PCA), Linear Discriminant Analysis (LDA) Independent component analysis, Random component analysis, and t – SNE.

Experience working on Natural Language Processing (NLP) techniques like Word2Vec, BOW (Bag of Words), TF–IDF, Avg-Word2Vec, IF-IDF, Weighted Word2Vec and used Sentiment Analysis to determine the emotional tone behind the series of words and gain the express of the attitudes to analyze the market of a product, customer service, fraudulent activities.

Expert level mathematical knowledge on Linear Algebra, Probability, Statistics, Stochastic Theory, Information Theory, and logarithms.

Strong experience with Python and its libraries Pandas, NumPy, Sci-Kit learn, Seaborn, Matplotlib and R for algorithm development, data manipulation, analysis, and visualization.

Experience working with different file formats like JSON, CSV, XML in Anaconda Navigator, Jupyter Notebook, Visual Studio code, and Spyder. Experience in using Git and Git Hub for source code management.

Expertise in transforming business requirements into analytical models, designing algorithms, building models, developing Data Mining and reporting solutions that scale across a massive volume of structured and unstructured data.

Tools & Technologies:

Languages

Python, R, SQL

Packages

Pandas, NumPy, SciPy, Scikit-Learn, Matplotlib, Seaborn, NLTK, Tensor Flow, Keras

Database

MySQL, PostgreSQL, DynamoDB, Mongo DB

Cloud Services

Amazon Web Services (AWS)

Mathematical Skills

Statistics, Linear Algebra, Probability

Machine Learning Algorithms

Linear Regression, Logistic Regression, Linear Discriminant Analysis (LDA), Decision Trees, Random Forests with Adaboost and Gradient Descent Boosting, Support Vector Machines (SVM), Naïve Bayes, K – Nearest Neighbor, Hierarchical clustering, K-means clustering, Density-based clustering (DBSCAN).

Machine Learning Techniques

Principal Component Analysis, Single Value Decomposition, Data Standardization Techniques, L1 and L2 regularization, RMS prop, Hyperparameter tuning, KL Divergence, Resampling Techniques like SMOTE, Cluster Centroid Methods, Ensemble Methods, Feature selection, and Feature Engineering, Cross-Validation Methods(K-fold), Bleu Score.

Deep Learning

Convolution Neural Network, Recurrent Neural Network, LSTMS, GRU, Autoencoders, Generative Adversarial Neural Networks, Policy-based and Value-based Boltzmann Machines.

Professional Experience:

Data Scientist/Machine Learning Engineer

Walmart, Bentonville, AR July ’18 – Present

Analyzed the customers' purchase data and product trends to recommend the types of products/services to customers based on their behavior tracked through the customer accounts, purchase history and location. Acquired years of sales data from relevant and novel data sources using data wrangling queries to understand the customer purchase patterns in different quarters.

Developed a machine learning system that predicted purchase probability through offers based on customer's real-time location data and past purchase behavior. These predictions are being used for mobile coupon pushes.

Developed a system that collects data across thousands of locations to optimize product placement and advertising to catch the eye of shoppers that fit the right profile and selection of products to be removed entirely to reduce clutter and make it easier to find in-demand items automatically.

Built one-class Support Vector Machine (SVM) and Principal Component Analysis (PCA) algorithms for anomaly detection of fraud and other errors that signal dishonest behaviors. Worked on Time Series Model building for Future Sales forecasting with Arima and Kera's Deep Learning techniques.

Forecasted sales and improved accuracy - (MAPE and RMSE) by 30% by implementing advanced forecasting algorithms that were effective in detecting seasonality and trends in the patterns.

Implemented a rule-based expertise system from the results of exploratory analysis and information gathered from the people from different departments.

Used Machine Learning Algorithms in python by importing Sci-kit learn, SciPy, NumPy, Pandas modules to analyze the data.

Evaluated the performance of different models using F-score, AUC/ROC, Confusion Matrix and RMSE/MSE and used Matplotlib, ggplot extensively to generate human-readable data visualizations.

Performed text analysis on the reviews of the products using NLP techniques like Bag of Words, Term Frequency-Inverse Document Frequency, Word2vec, Average Word2vec with help of NLTK library and Gensim package.

Design and develop analytics, machine learning models, and visualizations that drive performance and provide insights, from prototyping to production deployment and product recommendation and allocation planning.

Data Scientist/Machine Learning Engineer

SunTrust Bank, Atlanta, GA Mar ’17 – May ’18

Analyzed the data using various machine learning algorithms whether to extend/not credit limit to an existing applicant and to approve/not new credit line to a new applicant will likely result in profit or loss based on various circumstances like credit history, utilization rate, income, age, location, hard enquiries & number of deliquesces.

Ensured that the model has low False Positive Rate and Text classification and sentiment analysis for unstructured and semi-structured data.

Validated different models developed applying appropriate measures such as k-Fold cross-validation, Log loss function, Area under the ROC Curve (AUC), Relative Operating Characteristic curve (ROC) and Confusion Matrix to identify the best performing model.

Implement a chatbot using api.ai to improve customer interaction with the application.

Involved in the development of algorithms for fraud detection, customer churn prevention, lifetime value prediction, product development and prediction analysis based on company requirements and goals.

Worked on Descriptive, Diagnostic, Predictive and Prescriptive analytics.

Used Pandas, NumPy, Seaborn, SciPy, Matplotlib, Scikit-learn, and NLTK in Python for developing various machine learning algorithms.

Performed model Tuning by adjusting the Hyperparameters and raised the model accuracy.

Created and designed reports that will use gathered metrics to infer and draw logical conclusions of past and future behavior using Tableau.

Tackled highly imbalanced fraud dataset using under-sampling, oversampling with SMOTE and cost-sensitive algorithms using Python Sci-kit Learn.

Data Analyst

Sonata Software Solutions, Hyderabad, India Aug ’15 – Dec ’16

•Was a liaison between project teams, data architecture, data management, data stewardship, lines of business & the delivery/development group to align business needs with enterprise data management strategy & solutions. Worked with data scientists to support model building, scoring, monitoring, and reporting.

•Performed Exploratory Data analysis (EDA) to maximize insight into the dataset, detect the outliners and extract important variables by graphically and Numerically.

•Performed analytical modeling, database design, data analysis, regression analysis, data integrity, and business analytics.

•Performed data visualization and Designed dashboards with Tableau, and generate complex reports, including charts, summaries, and graphs to interpret the findings to the team and stakeholders.

•Prepared and analyzed the data includes locating, profiling, cleansing, extracting, mapping, importing, transforming, validating or modeling. Generate dashboards to provide trending of data using statistics and KPI's.

•Involved in writing SQL queries, triggers, stored procedures, CTE, sub-queries, temp tables, table variables, Joins, functions, views, Dynamic SQL, Cursors as per requirement and maintaining it in database.

•Partner with Risk and Decision Management organizations to understand the source of new data and continue to improve the process of defining, extracting and utilizing the new data

•Categorizing and Generating a report on the multiple parameters (i.e. Probability of Loss, Loss of Given Default, Expected Loss, Present Value Expected Loss, Credit Modelling) using MS Excel, Power BI, Python, etc.

Junior Data Analyst

Yash Technologies, Hyderabad, India May ’14 – Aug ’15

Involved in creating stored procedures and SQL queries to import data from SQL server to the Tableau.

Designing, developing and optimizing SQL code.

Performed extensive data mining and formulated SQL procedures for client database management.

Created SQL Queries for Portfolio Drill Downs to pull necessary data from data sources.

Extracting the source data from Oracle tables, MS SQL Server, sequential files and excel sheets.

Created Data Quality Scripts using SQL to validate successful data load and the quality of the data. Created various types of data visualizations using Python and Tableau.

Performed tuning on existing SQL queries for better optimization

Acquired strong experience in all areas of SQL server development including tables, user functions, views, indexes, stored procedures, functions, joins.

Education:

Master of Science in Management Information Systems, University of Illinois

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