Data Scientist Machine Learning

Uday Kiran Reddy Kotha

Data Scientist

Greenbelt, MD +1-475-***-**** ******@***********.***

Summary

Data Scientist with 3+ years of experience in leveraging advanced analytics and machine learning techniques to derive actionable insights from

complex datasets. Proven track record of developing predictive models, optimizing decision-making processes, and driving strategic initiatives.

Adept at collaborating with cross-functional teams and effectively communicating technical findings to both technical and non-technical

stakeholders.

Education

Master in Data Science University of North Texas, Denton, TX

Bachelor in Mechanical Engineer SCSVMV University, Kanchipuram, Tamil Nadu

Certification

LinkedIn: Generative AI and LLMOps: Building Blocks and Applications.

Skills

Methodologies: SDLC, Agile, Waterfall

Language: Python, R, SQL, SAS, Java, C++, Scala, PySpark

IDEs: Visual Studio Code, PyCharm, Spyder

Business Intelligence / Visualization / Other Tools: Meta Bar, Power BI, Tableau, Microsoft Excel, Jupyter Notebook

ML Algorithm: Linear Regression, Logistic Regression, Decision Trees, SVM, Random Forests, Naive Bayes, KNN, K Means, CNN, RNN

Python Libraries: NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, Seaborn, TensorFlow, ggplot2

Bigdata and Cloud Services: Alteryx, Hadoop, Spark, Kafka, AWS, GCP, Microsoft Azure

Frameworks & Tools: Jira, Flask, Django, Git, GitHub, Docker, CI/CD pipelines, Jenkins

Database and Warehouses: MySQL, PostgreSQL, Oracle, Big Query, MongoDB, Snowflake

Other Skills: Structured Data, Unstructured Data, Machine Learning, NLP, EDA, ETL Process, MLOps, Communication Skill, Meta Base,

Databricks, Data Cleaning, Data Wrangling, Critical Thinking, Communication Skills, Problem-Solving, Presentation Skills, Supervised Learning,

Unsupervised Learning, Classification, Data Visualization, Data Modeling, LLM Model, Predictive Analytics, Pattern Recognition, JMP, Data

Integration, SPSS, Quantitative Data, Data Science, Statistics, Statistical Analysis, Data Analytics, Artificial Intelligence, Autoregressive

Integrated Moving Average (ARIMA)

Operating System: Windows, Linux

Experience

Metlife, USA Data Scientist July 2023 - Current

• Developed predictive models to forecast key financial metrics, resulting in a 15% improvement in forecast accuracy.

• Implemented anomaly detection algorithms for fraud detection, reducing fraudulent transactions by 25%.

• Developed predictive models using machine learning algorithms, such as logistic regression and random forests, to forecast customer churn

predictions.

• Visualizing and presenting dashboards to stakeholders using Tableau by utilizing various plotting techniques.

• Implemented Logistic Regression, GBT Classifier in PySpark for customer churn prediction, achieving 83% accuracy.

• Conducted hypothesis testing and statistical analysis to validate model performance and identify areas for improvement, leading to iterative

enhancements in fraud detection capabilities.

• Refined models by 13% via advanced feature engineering, data augmentation, hyper-parameter tuning with Grid Search CV.

• Managed source code on GitHub, deployed on Apache Spark cluster to optimize performance by 72% for large scale data.

• Working with Pandas, NumPy, and Matplotlib for developing various algorithms.

• Designing and maintaining MySQL databases, and creating pipelines using user-defined functions and stored procedures for daily reporting tasks.

• Participated in all phases of data mining, data collection, data cleaning, developing models, validation, and visualization.

Maruti Techlabs, India Data Scientist March 2020 - July 2022

• Utilized the Agile methodology of development for requirements, planning, design, and deployment.

• Cleaned and manipulated complex datasets using Python, SQL, Tableau, and Excel, improving data processing efficiency by 20%.

• Applied machine learning algorithms and advanced statistical analysis (decision trees, regression models) with Python’s scikit-learn, resulting in

a 15% improvement in predictive accuracy.

• Evaluated model performance with metrics like R square, confusion matrix, AUC, ROC curve, and RMSE, ensuring accurate insights.

• Performed Exploratory Data Analysis (EDA) with data visualizations (Matplotlib, Seaborn) and hypothesis testing, enhancing data-driven

decision-making.

• Extracted and processed Big Data into Hadoop HDFS from various sources, optimizing data storage and retrieval processes.

• Conducted data pre-processing, including data cleaning and feature engineering, leading to a 25% reduction in missing values through data

imputation techniques.

• Achieved 8% revenue boost by conducting exploratory analysis of market trends and optimizing customer - centered KPI.

• Generated employee’s performance insights using interactive dashboards in Tableau to enhance business efficiency by 17%.

• Collaborated with cross-functional teams, delivering detailed analytical reports, meeting data requirements on time.

• Implemented Azure Data Factory pipelines for automated data ingestion and transformation, improving data processing efficiency by 30%.

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