Bhavitha Peddamaru

Profile

Data Scientist with *+ years of hands-on experience translating

complex business problems into actionable insights. Master of Business Analytics student honing analytical and technical skills to drive innovation. Seeking a challenging opportunity to leverage deep technical expertise and business acumen to lead complex data science projects and contribute to organizational growth.

Employment History

Senior Data Scientist at Zebra Technologies, Bangalore June 2022 — August 2022

• Built a Lead generation model to boost sales of an online education service company using logistic regression to identify the most promising leads i.e. the leads that are most likely to convert into paying customers

• Improved accuracy by 30% over the heuristic-based approach.

• Built LifeTime Value (LTV) prediction model using linear regression.

• Used estimates from the LTV model to focus ad campaigns on high-value customers.

Data Scientist at Accenture Solutions Pvt. Ltd., Hyderabad May 2017 — June 2022

• Designed an ETL framework for moving data from transactional data sources into SSMS (SQL Server Management Studio) using AZURE cloud services for supporting the data requests from the analytics team.

• Performed Demand forecasting of a retail client at various geographical locations. The model was based on generalized linear models. This solution saved around 1 Million USD in operational costs in logistics.

• Created segmentation models using K-means Clustering for exploring potential user segments.

• Generated large and complex data extracts and queries for the Analytical Leads for data analysis by utilizing Microsoft SQL Server database schema

• Conducted analytical deep-dives to analyze complex problems and opportunities, identifying hypotheses, and designing and executing experiments that led to valuable insights and actionable recommendations.

• Demonstrated proficiency in writing and debugging Python scripts.

• Extracted actionable insights through the analysis of large, complex, multi-dimensional customer behavior data sets, enabling the company to optimize its strategies and improve customer experiences.

Details

408-***-****

ad4yxl@r.postjobfree.com

Links

Linkedin

Skills

Data Analysis

MySQL

Deep Learning

Machine Learning

Extract Transform Load (ETL)

JavaScript (Programming

Language)

Big Data

Management

Power BI

HyperText Markup Language

(HTML)

Azure Cloud Services

R (Programming Language)

Text Mining

Quantitative Analysis

Python (Programming

Language)

Data Visualization

SQL (Programming

Language)

Database Schema

Debugging

Statistical Modeling

NoSQL

Forecasting

Data Mining

• Produced insightful visualizations and reports to communicate methodologies and results effectively.

• Developed and delivered quality Power BI reports to the users by conducting quality assurance checks, fixing the reported issues within SLA time and recorded 70% increase in user satisfaction. Education

Master of Science in Business Analytics, California State University East Bay, Hayward

August 2023 — December 2024

• Proficient in data analysis and manipulation using tools such as SQL, Python, and R.

• Strong foundation in probability and statistics, with hands-on experience in experimentation design, A/B testing, ANOVA testing and probabilistic modeling.

• Good understanding of Deep Learning frameworks (e.g., TensorFlow and PyTorch)

• Knowledge of NLP concepts, including tokenization, stemming, and word embeddings

• Familiarity with NLP libraries and tools (NLTK, spaCy, Gensim)

• Strong understanding of recurrent neural networks (RNNs) and convolutional neural networks (CNNs)

• Strong foundation in Math ( Linear Algebra, Calculus ) Projects:

Store Sale Forecasting -

Championed a Kaggle project to estimate Ecuador country's store sales using random forest regression technique. Achieved significant improvement in forecasting the sales with RMSLE value of 0.45 Sentiment Analysis for Movie Reviews -

Developed a Python-based sentiment analysis model using a recurrent neural network (RNN) to classify movie reviews as positive or negative. Preprocessed text data, tokenized sentences, and applied word embeddings.

Text Generation using LSTM -

Created a basic text generation model using Long Short-Term Memory

(LSTM) networks. Trained the model on a dataset of Shakespearean sonnets to generate coherent and contextually relevant text sequences. Named Entity Recognition (NER) System -

Implemented an NER system using spaCy to extract entities like names, locations, and organizations from unstructured text. Fine-tuned the spaCy model to improve NER accuracy.

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