Uday Kiran Chilakala

Data Scientist

Atlanta, GA 678-***-**** ad4x10@r.postjobfree.com

SUMMARY

• Data Scientist with Around 5 years of experience in Data Extraction, Data Modelling, Data Wrangling, Statistical Modeling, Data Mining, Machine Learning, and Data Visualization.

• Experience in agile and waterfall project management methodologies to produce high-quality deliverables that meet or exceed timeline and budgetary targets.

• Experienced with machine learning algorithms like logistic regression, random forest, KNN, SVM, neural network, linear regression, lasso regression, and k-means.

• Proficient in Statistical Methodologies including Hypothetical Testing, Time Series, Cluster Analysis, and Discriminant Analysis.

• Knowledge of Natural Language Processing (NLP) algorithms and Text Mining.

• Proficient in Python with SciPy Stack packages including NumPy, Pandas, SciPy, Matplotlib, and Python.

• Good knowledge in provisioning virtual clusters under the AWS cloud which includes services like EC2, S3, and EMR.

• Knowledge in Database Creation and maintenance of physical data models with Oracle, MongoDB, and SQL Server databases. SKILLS

Methodologies: SDLC, Agile, Waterfall

Language: Python, R, SQL, SAS

IDEs: Visual Studio Code, PyCharm, Jupiter Notebook Statistical Methods: Hypothetical Testing, ANOVA, Time Series Machine Learning: Regression analysis, Bayesian Method, Decision Tree, Random Forests, Support Vector Machine, Neural Network, Sentiment Analysis, K-Means Clustering, KNN, Classification, SVM, Naive Bayes, Natural Language Processing (NLP), LLM, CNN, XGBoost

Packages: NumPy, Pandas, Matplotlib, SciPy, ggplot2, Scikit-Learn, PyTorch, TensorFlow, Keras, Spark Visualization Tools: Tableau, Power BI, Microsoft Excel Cloud Technologies: AWS, GCP

Database: MySQL, SQL Server, Oracle, MongoDB

Software/Other Skills: Jira, Data Cleaning, Data Wrangling, Critical Thinking, Communication Skills, Presentation Skills, Problem- solving, Decision-Making, EDA, Communication Skill, Databricks, Data Visualization, Predictive Analytics, Pattern Recognition, JMP, Data Integrity, Quantitative Data, Data Science, Statistics, Statistical Analysis, Data Analytics, Data Modeling, Big Query, Snowflake Operating System: Windows, Linux

EDUCATION

Master in Computer Science

Kennesaw State University, Marietta, Georgia

Bachelor in Electronics and Communication Engineering Vardhaman College of Engineering, Hyderabad, Telangana WORK EXPERIENCE

PNC Financial Services Group, USA Data Scientist Oct 2022 - Current

• Participated in Agile ceremonies such as sprint planning, daily stand-ups, sprint reviews, and retrospectives to facilitate iterative development and continuous improvement.

• Worked on Natural Language Processing with NLTK module of python for application development for automated customer response.

• Used Python (NumPy, SciPy, pandas, seaborn) & Spark (PySpark) to develop a variety of models and algorithms for analytic purposes.

• Developed and implemented predictive models using machine learning algorithms such as linear regression, classification, multivariate regression, Naive Bayes, Random Forests, K-means clustering, KNN, PCA, and regularization for data analysis.

• Performed analysis of Cloud SQL and used Python to connect current data to Google Big Query, making data transfer possible within only a few microseconds.

• Designed and implemented recommender systems that utilized Collaborative filtering techniques to recommend courses for different customers and deployed to the AWS EMR cluster.

• Utilized natural language processing (NLP) techniques to Optimized Customer Satisfaction.

• Conducted data cleaning and data analysis using Tableau to monitor app operation status, order stability, improving response effectiveness by 23%.

• Created monthly performance-based client billing reports using MySQL and NoSQL databases. Innoventix Solutions, India Data Scientist Nov 2018 - Dec 2021

• Conducted thorough testing and validation of data science models and solutions before deployment, following a structured and rigorous approach typical of Waterfall methodologies.

• Designed and developed data wrangling and visualization techniques as well as a classification engine based on Logistic Regression.

• Extensively use Python's multiple data science packages like Pandas, NumPy, Matplotlib, Seaborn, SciPy, Scikit-learn, and NLTK.

• Apply various machine learning algorithms and statistical modelings like decision trees, text analytics, natural language processing

(NLP), supervised and unsupervised, regression models, social network analysis, neural networks, deep learning, SVM, and clustering to identify Volume using scikit-learn package in python, Matlab.

• Utilized advanced methods of big data analytics, machine learning, artificial intelligence, wave equation modeling, and statistical analysis.

• Integrated Power BI with Microsoft Excel and other data sources to create reports that update automatically with new data.

• Designed A/B testing frameworks to test efficacy of products and interventions designed to help students.

• Leveraged AWS EC2 (Elastic Compute Cloud) instances for running data processing tasks, model training, and inference, utilizing scalable computing resources to handle variable workloads.

• Developed Deep Learning model for industrial decision-making process through Keras - Models such as RNN and LSTM are implemented as well.

• Implemented backup and recovery strategies for SQL Server databases, ensuring data availability and integrity in case of system failures or disasters.

PROJECTS

Logistic Regression for SUV Predictions June 2022 - July 2022

(NumPy, Pandas, Matplotlib, Scikit-learn).

• Created a model to predict whether customers will make a purchase based on their age and estimated salary.

• Data cleaning is done by dropping unwanted features and cross-validation is used to split the data.

• Input values are scaled down, and values are predicted using logistic regression which produced an accuracy of 89%. Drowsiness detection using Raspberry Pi Jan 2020 - May 2020

(Raspbian OS, Python 3.6, OpenCV, RPi.GPIO, Dlib, Euclidean).

• The system employs a real-time, non-intrusive approach to monitor the driver's eyes, mouth, and yawning patterns using a Raspberry Pi mini-computer and Python with OpenCV.

• It aims to provide accurate drowsiness detection, alerting the driver through alarms and, if necessary, notifying the owner or authorities via calls or SMS.

• The project's modular structure includes image acquisition, a main processing system with machine learning algorithms, GSM/Calling capabilities, and an alert-raising module.

• With a focus on user-friendliness and affordability, this solution aims to significantly contribute to road safety by preventing accidents caused by drowsy or alcohol-impaired drivers.

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