Spandana S

Location: Columbus, Ohio Phone: 312-***-**** Email: ada9nv@r.postjobfree.com

linkedin.com/in/spandana-sudalagunta github.com/spandanasudalagunata

PROFESSIONAL SUMMARY

Possess 2 years of experience in Data Mining, Advanced Analytics, Predictive modeling, NLP/ AI/ Machine Learning/ Deep Learning/ Probabilistic Graphical Models, Data analysis/reporting, Data Validation and Data Visualizations using Python, R and Tableau, Microsoft Power BI and did Version Control with GIT

Skillful at slicing and dicing in large datasets of Structured and Unstructured data, Data Acquisition. Experience with file systems, server architectures, databases, SQL, and data movement (ETL).

Proficient in Machine Learning techniques (Decision Trees, Linear, Logistics, Random Forest, SVM, Bayesian, XG Boost, K-Nearest Neighbors) and Deep Learning techniques (CNNs, RNNs, LSTMs) and Statistical Modeling in Forecasting/ Predictive Analytics, Segmentation methodologies, Regression based models, Hypothesis testing, Factor analysis/ PCA, Ensembles.

Python, NumPy, Scikit-Learn, genism, NLTK, TensorFlow, keras.

Hands on experience in implementing LDA, Naive Bayes and skilled in Decision Trees, Random Forests, Linear and Logistic Regression, SVM, Clustering, neural networks and good knowledge on Recommender Systems.

Data-savvy with strong verbal and written communication skills, learnt by networking and interacting; Excellent behavioral and interpersonal skills, team-player, can manage multiple tasks, critical thinker.

TECHNICAL SKILLS

Programming languages, Tools & Concepts: SQL, R, Python, SAS, NOSQL, Machine learning (Supervised and unsupervised learning), ARIMA, Statistical Modelling, Text Analysis, variance Analysis, Data Analytics in Microsoft Excel, Visualization, Agile Methodologies, SQL Server, Recommendation systems, Market-basket-analysis, Classification, Regression, Clustering, AWS, Cloud Computing, Jupyter Notebook, Net Logo, WEKA.

Business Intelligence Tools: Tableau, Jump (JMP), Microsoft Power BI

PROFESSIONAL EXPERIENCE

Jr Data Scientist

ANDWILL LLC, Wilmington, Delaware Jan 2019 – Present

Implemented Machine Learning, Deep Learning and Neural Networks algorithms using TensorFlow Deep Learning Framework and designed Prediction Model using Data Mining Techniques with help of R, Python.

Gathering, reviewing business requirements and analyzing data sources.

Conducted data cleaning, data preparation, and outlier detection.

Created executive dashboards to show the patterns & trends in the data using Tableau Desktop and developed different visualizations and dashboards using advanced features and deep analytics in Tableau.

Remote Data Science Trainee

Web Business Look, Houston, Texas Dec 2017 – Dec 2018

Assisting business by being able to deliver a machine learning project from beginning to end, aggregating and exploring data, building and validating predictive models and deploying completed models to deliver business impacts to the organization

Performed Data Cleaning, features scaling, features engineering using Pandas and NumPy packages in Python and build models using Predictive Analytics

Analyzed raw data to unearth the hidden business information using industry standard analytical tools. Wrote SQL to extract data and perform data processing and data mining to improve the data quality by 60%.

Worked collaboratively with data analysts to identify opportunities to add value through advanced analytics. Executed dashboards to deliver actionable insights to assist decision-making.

EDUCATION

Master of Science in Data Science (courses include Statistics & Machine learning) Aug. 2019

Kent State University, Kent, Ohio GPA: 4.0/4.0

Liberty University 2018 (Mathematical Statistics and Probability, Principals of Management) – undergraduate GPA: 4.0/4.0

Bachelor of Science in Dental Surgery - Dr. NTR University of Health Science, Andhra Pradesh, India Aug. 2016, GPA: 3.76/4.0

PROFESSIONAL CERTIFICATIONS

IBM Data Science Professional Certificate

ACADEMIC PROJECTS

Capstone project Jun – Aug 2019

Partnered with Diebold to build a predictive model that can predict if a particular work was billable or not.

Recoded and transformed variables to tensors to build the model.

Built deep learning neural network using R for Predictive analysis.

Evaluated models and generated/automated reports based on insights available from the data.

Student Retention Feb – Apr 2019

Worked with the Kent state University to develop effective data-driven strategy for student retention, student prospecting, graduation and retention rates, offer optimization etc.

Identified opportunities for increasing retention, marketing profitability, by exploring and analyzing of student behavioral data. In charge of statistical modeling operations for the project. Calculated the accuracy and took steps to improve the performance of the model.

Risky Lending Business Jan - Mar 2019

Worked on a project for Barclays bank for maximization of return-on-investment (ROI) on the issuance of small dollar loans. This optimization is supported through the accomplishment of two supporting goals: Determining the probability of default (PD)-logistic regression model, and loss given default (LGD)-linear regression model, of every applicant.

Credit card Fraud detection February 2019

Credit card fraud detection for a private credit card company using CRISP-DM. Performed PCA Transformation, feature selection. Built a tree-based model to predict fraud transactions and evaluated model using – AUC, Return on investment (ROI), Confusion matrix and statistics. Deployment.

Churn Prediction Aug - Dec 2018

Customer churn prediction on generic data from unknown US carrier. Performed feature selection and built Logistic regression model for predicting the churn probability. Used Tableau to create dashboards with plotted maps to present the insights. Model evaluation – AUC, Confusion matrix and statistics.

Developed SQL queries to generate weekly and monthly reports on customer claims and used data visualization to create real-time ROI graphs that helped to focus on high-profit business and deliver insights to the company on customer churn and acquisition.

Wells Fargo campus analytics Aug - Nov 2018

Built a machine learning algorithm that minimizes carbon footprint for each customer while maintaining their total quality of life by problem formulation, choosing the constraints, formulating the objective function. Performed optimization and created web application (shiny app) and presented the project with a live demo.

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