VINEETH APPALA

Data Scientist

+1-337-***-****

ady9pl@r.postjobfree.com

SUMMARY

Data Scientist with expertise in Python, R, and SQL. Proficient in Machine Learning, Deep Learning, Data Mining, NLP, and Data Analysis. Skilled in data manipulation and visualization using NumPy, Pandas, Matplotlib, and Tableau. Experienced in building recommendation models and matching data for improved accuracy. Adept at handling large datasets on cloud platforms like GCP and AWS. Proven success in developing Convolutional Neural Networks

(CNN) for image recognition and sentiment analysis using TensorFlow and R. Committed to driving data insights for business growth and enhancing decision-making processes.

EXPERIENCE

Cognizant — Data Scientist

Dec 2021 – Present

Project-1: Collaborative Filtering Recommendation Model

● Developed a Collaborative Filtering recommendation model for the company's website using clickstream data, enhancing customer experience.

● Managed and processed large volumes of data in BigQuery (GCP), employing advanced feature engineering and scaling techniques.

● Utilized Data Studio for effective data visualization, providing valuable insights to stakeholders.

● Leveraged Vertex AI (GCP) to preprocess data and construct recommendation models, resulting in accurate webpage recommendations for customers.

Project-2: Vehicle Parts Data Analysis

● Conducted comprehensive analysis of vehicle parts data, ensuring alignment between dealer and company requirements.

● Successfully preprocessed vast datasets from 13 different dealer regions, ensuring data accuracy and consistency.

● Achieved an impressive 67% match between dealer and company parts data, optimizing order list accuracy.

● Implemented n-gram techniques to identify order description patterns, enhancing data understanding and analysis.

● Utilized Levenshtein technique for precise matching of parts numbers in unknown data, streamlining part assignment process.

● Developed and Executed Test Cases using VersionOne Tool. SKILLS

Programming Languages:

Python, R, SQL

IDEs:

Pycharm, Jupyter Notebook,

Spyder, R Studio, Visual

Studio, Google Colab

Technical Skills:

Machine Learning, Deep

Learning, Data Mining,

Natural Language Processing

(NLP), Data Analysis, Data

Visualization, Data Munging,

Data Modeling, Statistics

Packages:

NumPy,Pandas,Matplotlib,Sci

Py,PySpark,Scikit-learn,Seab

orn,TensorFlow,Keras,Ggplot

2,NLTK,dplyr,tidyr

Visualization Tools:

PowerBI,Tableau,Microsoft

Excel

Cloud Platform:

AWS, GCP

CERTIFICATIONS

● AWS

● GCP

● Machine learning

● Deep learning

● Thoroughly managed the defect life cycle by reporting and validating defects.

● Utilized SQL queries to validate data and ensure report accuracy by comparing data using the File comparison tool ExamDiff.

●

Watergrass: The database for growing nonprofits —

Business Analyst

March 2021 – May 2021

● Analyzed data from 30+ organizations spanning over 40 years, utilizing advanced statistical techniques.

● Conducted preliminary data analysis, identifying and resolving anomalies, including duplicate removal and missing value imputation.

● Performed feature engineering and feature scaling techniques to enhance model performance.

● Created user-friendly and interactive dashboards using PowerBI, providing actionable business insights.

● Developed ML models to predict donation amounts based on donor characteristics, optimizing fundraising strategies.\ Nuronics Labs Pvt. Ltd.— Data Scientist

May 2017 – May 2019

● Analyzed DICOM files representing organs and tissues within the human body for 1000 patients, totaling up to 150,000 files.

● Processed DICOM files to extract essential image data.

● Implemented image enhancement techniques to reduce noise and interference, enhancing accuracy.

● Collaborated closely with the product team to develop a Python-based Convolutional Neural Networks (CNN) model.

● Achieved 80% training accuracy and 76% testing accuracy in lung cancer cell detection.

● Designed and developed an Image Recognition model for a NEWS channel using TensorFlow, focusing on image labels and web entities.

● Utilized Neural Nets to identify crucial features within the images.

● Employed Data Augmentation techniques, including shear, zoom, and rotation, to generate additional data.

● Streamlined image classification using multiple Convolution and MaxPooling layers.

● Trained the data with CNN, employing dropouts to control overfitting and enhancing model performance.

● Achieved a 30% improvement in performance speed using a GPU for computational e ciency.

● Extracted text data and performed necessary pre-processing techniques for analysis.

● Conducted sentiment analysis using the AFINN lexicon, utilizing R programming for insightful sentiment assessment. EDUCATION

University of New

Hampshire, NH, USA

Peter T. Paul College of

Business and Economics

Master of Science in

Business Analytics

August 2019 - May 2021

Manipal University

Jaipur, India

Bachelor of Technology

in Electronics and

Communication

Engineering

August 2015 – May 2019

PERSONAL SKILLS

Languages: English, Hindi,

Telugu

Interests: Dancing, Soccer,

Cricket, Badminton and

Gaming

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