Manjunath Reddy Karri Venkata Contact: 610-***-****

linkedin.com/in/manjunath-reddy-karri-venkata-733253118/ adw3lg@r.postjobfree.com EDUCATION

Pennsylvania State University (PSU) Malvern, PA

Master of Professional Studies in Data Analytics (CGPA: 4.0/4.0) December 2023 Research Assistantship: I am currently collaborating with a team of five other research associates at the Big Data Labs, Pennsylvania State University, under the guidance of Professor Dr. Raghu Sangwan to design, build, and implement a universal recommendation system that can be tailored for the e-commerce, entertainment, and social media industries, providing customers with personalized recommendations akin to Amazon's personalized recommendation services. Coursework: Applied Statistics, Data Mining, Database Design Concepts, Analytics Programming in Python, Data Driven Decision Making, Deep Learning, Large Scale Database & Warehousing, Predictive Analytics R V College of Engineering (RVCE) Bengaluru, India Bachelor of Engineering in Electronics and Communication Engineering May 2018 SKILLS

Programming Languages: Python, R, SQL, VBA, App Scripts Skillsets: Big Data, Statistical Data Analysis, Data warehousing, Data Mining, Data Modeling, Data Visualizations ML Libraries: PyTorch, Numpy, Pandas, Scikit-learn, TensorFlow, Keras ML/ Deep Learning Models: CNN, RNN, BERT, GPT3, ResNet, ConvNext, Random Forest, Decision Trees, LR BI Tools: Tableau, Quick Sight, Google Data Studio and R Shiny EXPERIENCE

Goto Financial, Sr. Data Analyst Nov 2021 – July 2022 Talent Acquisition and HR Data Analytics Team

• Devised and executed data gathering methods and tactics to enhance the statistical efficiency and accuracy of Talent Acquisition

& HR data collected from the ATS tool, Lever.

• Streamlined the application and resume screening process for recruiters by developing a classification model that categorizes incoming applicants into potential high achievers based on their years of experience, education, and skill sets, using prior employee data within the company. This initiative reduced recruiter time for resume screening by 40%.

• Created data pre-processing pipelines and developed 12 recruitment and operations dashboards utilizing Google Data Studio. Amazon, Data Analyst Aug 2020 – March 2021

Global Trade Business Intelligence Team

• Designed and developed seller performance Quick sight dashboard to aid business intelligence team at Amazon Seller Services in measuring, understanding, and tracking growth of sellers in Amazon Marketplace.

• Built ETL pipeline queries to extract, transform and load data from Amazon redshift clusters to create weekly and monthly performance reports.

• Actively participated in requirements meetings and data mapping sessions to understand and plan future business needs.

Mu Sigma, Trainee Decision Scientist June 2018 – July 2022 Digital Impressions & Marketing Analytics Team

• Managed and optimized digital marketing ad-spends by leading a team of 7 trainee data scientists.

• Assisted the Director of Marketing Analytics at a Fortune 500 company with customer understanding by building customer frameworks and RFM models for targeted marketing using web analytics tools like Adobe Analytics and Google Analytics.

• Extracted holiday season shopping trends through data mining techniques to help category managers manage inventory effectively.

• Automated weekly and monthly business review reports using R and Visual Basics in Microsoft Excel, reducing report generation time from two hours to just ten minutes.

• Conducted customer segmentation analysis based on recency and frequency, providing strategy recommendations for targeted and personalized marketing.

Coursera and IBM Professional Certifications

• Data Science Orientation (Credly Badge : https://www.credly.com/badges/0141ab05-f936-48ca-be7d-c11c12de3c4b/public_url )

• Tools for Data Science (Credly Badge : https://www.credly.com/badges/fcb74354-3c6e-4bf2-a0e7-5ff54e2a7f22/public_url )

• Data Science Methodology (Credly Badge : https://www.credly.com/badges/e443c744-cb5b-4aca-8e2b-b3999143d8a0/public_url ) Achievements

• Won Best Paper Award at National Level Competition “Make in India” conducted by Entrepreneur Cell of India at Chennai 2017 for the paper titled “The Smart Shoes”.

• Honored with spot awards as cited by Clients and Managers,” Sheer dedication and ownership shown in every challenging Ad-hoc and Project.”

• Semi-finalist in the Nittany AI Challenge 2023, a prestigious competition that recognizes outstanding talent in the field of AI. My team and I were selected based on our innovative project, "bAIbyBot" which provides new parents with AI-powered. PROJECTS

Weather Forecasting using Ensemble Predictive Modelling (PSU Spring 2023)

• Developed an Ensemble Predictive Model utilizing XGB and SARIMA algorithms for time series weather forecasting across multiple US locations, achieving an impressive 93% accuracy. Validated the model's performance through rigorous 10-fold cross validation.

Deep OCR Model for Tabular Data Extraction from Images (PSU Spring 2023)

• Created a Deep Learning model by implementing transfer learning with pre-trained encoders such as VGG19, ResNet50, and Densenet121 in the Encoder-Decoder architecture. Additionally, incorporated custom-built CNN layers for table and column detection, and utilized tesseract OCR python libraries for effective text extraction. This model can efficiently extract text information, such as nutritional information from baby food labels, aiding in tracking nutritional intake. Movie Recommender System: Content and Collaborative Filtering approach (PSU Fall 2022)

• Recommending movies with similar content like plot, Genre, Cast and Keywords achieved by TF-IDF and cosine similarities. Additionally, recommendations based on identical user preferences by grouping or clustering like-minded users using KNN.

Email Marketing Ad Clicks Prediction model (PSU Fall 2022)

• Came up with best prediction model for an e-commerce digital marketing team to predict the click ability of various marketing advertisements with 80 % accuracy and precision (Various classification techniques like KNN, Random Forest, Logistic Regression, Naive Bayes, and Decision Trees were built and compared with model outputs). Database and Design: NextGen Restaurant (PSU Fall 2022)

• Created a database (Involves conceptual, Logical and Physical Design) for a restaurant system to enhance and optimize restaurant capabilities such as customer seating, reservations, staff management, and coordination, as well as to assist management with reporting and analytics.

Credit Risk Analysis: Home Credit Group (PSU Fall 2022)

• Assessing credit worthiness of a new applicant using classification techniques to predict credit score based on credit history of similar previous customers (Various Supervised Classification techniques like KNN, Random Forest, Logistic Regression, Naive Bayes and Decision Trees were built and compared with model outputs) Text Analysis and Feature Engineering using NLP (PSU Fall 2022)

• Performed data pre-processing on unverified Covid-19 articles in pdf / word formats using NLTK package libraries like Brown, Gutenberg, Punkt, Treebank, Webtext, Wordnet and Stopwords to perform tokenization, Stemming, Stop Words, POS Tagging and Exploratory Analysis

• Identified amount of misinformation in unverified articles / blogs on Covid-19 by identifying the most popular misinformation keywords referred from the survey conducted by World Health Organization research. Job Applicant’s Resume Classification model (GTF Jan 2022)

• Pool of Job applications are classified based on text keywords matching between resume and job criteria / requirements present in job postings with 83% accuracy and precision, thus helping recruiters to cut down resume screening time from 40 to 2 hrs.

Seller Performance Framework - Statistical Modelling (Amazon Dec 2020)

• The Seller Performance Framework was built and developed to track and report individual performance of a seller despite variance in product mix and type of sellers.

Seller Classification Model (Amazon Feb 2021)

• Built Seller Classification model for Amazon Seller Central team with 89% Accuracy to identify and classify cross border sellers into various business groups based on time of engagement, Type of engagement, Performance, and other various business rules.

RFM Customer Frameworks - Statistical Modelling (Mu Sigma Feb 2019)

• Retail and E-Commerce Customers are segmented based on their purchase behavior on aspects like recency, frequency and monetary using bigdata query in Hive frameworks.

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