· Collaborated with Data and IT Architects on data movement and storage, using ER Studio 18.5 for modeling.· Developed and deployed machine learning algorithms with Python libraries (pandas, NumPy, SciPy, scikit-learn, NLTK).· Implemented supervised models (Logistic Regression, Decision Trees, KNN, Naive Bayes) to improve predictions.· Used the Caffe Deep Learning Framework for designing neural networks for image and video analysis.· Worked with data formats like JSON and… Show more · Collaborated with Data and IT Architects on data movement and storage, using ER Studio 18.5 for modeling.· Developed and deployed machine learning algorithms with Python libraries (pandas, NumPy, SciPy, scikit-learn, NLTK).· Implemented supervised models (Logistic Regression, Decision Trees, KNN, Naive Bayes) to improve predictions.· Used the Caffe Deep Learning Framework for designing neural networks for image and video analysis.· Worked with data formats like JSON and XML; utilized Python for manipulation and analysis.· Managed data mining phases: collection, cleaning, model development, validation, and visualization.· Created NLP systems for automating customer service query classification.· Designed 3NF and dimensional data models (Star and Snowflake Schemas) for operational and OLTP systems.· Built Azure-based solutions with Azure Event Hub, Stream Analytics, PowerBI, and Azure ML for real-time analysis.· Led Big Data integration in Microsoft Azure to enhance analytics capabilities.· Developed JSON scripts and managed Azure Data Factory (ADF) pipelines for ETL.· Updated Python scripts for AWS Cloud Search integration and document classification.· Built solutions using Azure PaaS for visualization and assessing business impact.· Guided Agile teams in application development and ensured HIPAA compliance for data security.· Developed R and Python programs for data preparation and harmonization.· Conducted gap analysis for data discrepancies and optimization.· Created OLAP databases, scorecards, dashboards, and reports for insights.· Managed data from various sources using Nexus, Toad, Business Objects, Powerball, and SmartView.· Utilized Seaborn and Matplotlib for data visualizations and automated ETL with Azure Data Factory.· Maintained documentation of data processes and models; conducted training for data science tools.· Evaluated new data analytics technologies to stay current. Show less

· Analyzed large datasets using Python, R, and Scala, leveraging TensorFlow and PyTorch for deep learning.· Managed big data with Apache Spark and Hadoop, using Spark MLlib and Hadoop MapReduce for scalable data operations.· Developed predictive models with XGBoost, RandomForest, and SVM to forecast customer behavior.· Conducted data profiling on traffic patterns and locations with Python and MATLAB.· Applied statistical modeling, including decision trees and regression models… Show more · Analyzed large datasets using Python, R, and Scala, leveraging TensorFlow and PyTorch for deep learning.· Managed big data with Apache Spark and Hadoop, using Spark MLlib and Hadoop MapReduce for scalable data operations.· Developed predictive models with XGBoost, RandomForest, and SVM to forecast customer behavior.· Conducted data profiling on traffic patterns and locations with Python and MATLAB.· Applied statistical modeling, including decision trees and regression models, to improve decision-making.· Implemented machine learning pipelines in Scikit-learn, TensorFlow, and Keras for automated model processes.· Designed real-time data processing pipelines with Kafka, Spark Streaming, and Flink for live data insights.· Created visualizations and dashboards using Tableau, PowerBI, and D3.js to support business decisions.· Cleansed, engineered features, and scaled data with Pandas and NumPy for analysis readiness.· Maintained SQL and NoSQL databases like Cassandra and MongoDB, ensuring data integrity.· Led A/B testing and data-driven campaigns to evaluate strategy effectiveness.· Optimized models with cross-validation, ROC curves, AUC; addressed overfitting using Lasso and Ridge.· Collaborated with teams to integrate machine learning into enterprise systems.· Wrote stored procedures and triggers in Oracle and PL/SQL to automate data workflows.· Managed end-to-end machine learning projects from data collection to deployment.· Developed shell scripts and used NZSQL/NZLOAD for efficient data loading.· Applied PCA for dimensionality reduction in high-dimensional data.· Conducted exploratory data analysis to identify key variables for model development.· Implemented rule-based systems for interpreting outputs and providing recommendations.· Analyzed user lifetime value using longitudinal data analysis.· Communicated technical results to non-technical stakeholders to influence decisions.· Enhanced data quality with validation scripts in SQL and Hive. Show less

· Collaborated with clients to gather data requirements and executed ETL processes to standardize formats.· Conducted advanced SQL queries for data retrieval and preliminary analysis.· Utilized Python's Pandas for data preprocessing, including cleaning, type casting, and table merging for EDA.· Applied feature engineering like PCA, normalization, and label encoding using Scikit-learn for high-dimensional data.· Explored data using correlation analysis and visualization tools… Show more · Collaborated with clients to gather data requirements and executed ETL processes to standardize formats.· Conducted advanced SQL queries for data retrieval and preliminary analysis.· Utilized Python's Pandas for data preprocessing, including cleaning, type casting, and table merging for EDA.· Applied feature engineering like PCA, normalization, and label encoding using Scikit-learn for high-dimensional data.· Explored data using correlation analysis and visualization tools like Matplotlib and Seaborn, focusing on healthcare data.· Developed and maintained data warehousing and Data Lake solutions, integrating SQL and NoSQL databases.· Analyzed large datasets with R, SAS, MATLAB, and Python, focusing on linear regression models.· Executed pattern recognition on financial time series data using ARMA, ARIMA, and exponential smoothing.· Designed and tested predictive models like Logistic Regression, SVM, Random Forest, XGBoost, and Neural Networks.· Managed end-to-end data workflows, including collection, storage, analysis, and model validation.· Performed data wrangling, cleaning datasets, and visualizing trends using Python and Matplotlib.· Implemented and optimized predictive models on AWS Lambda, selecting the best algorithms for performance.· Gathered post-deployment feedback to retrain models and improve accuracy.· Developed and maintained reports using Tableau, influencing business decisions.· Mentored junior data scientists, leading complex data projects.· Streamlined data pipelines using Apache Spark and Hadoop, enhancing processing efficiency.· Strengthened data security through governance and quality control measures.· Integrated AI and machine learning into broader tech stacks and business applications.· Contributed to developing proprietary algorithms, translating complex data into actionable insights. Show less

· Engaged in the full Software Development Life Cycle (SDLC), analyzing business requirements and mapping workflows using Agile methodologies.· Completed an advanced Data Science program, focusing on Data Manipulation, Visualization, Machine Learning, Python, SQL, and cloud platforms like AWS and Azure.· Utilized Python libraries (Pandas, NumPy, Matplotlib, scikit-learn) to develop and fine-tune machine learning models, processing complex data formats like JSON and CSV.· Performed… Show more · Engaged in the full Software Development Life Cycle (SDLC), analyzing business requirements and mapping workflows using Agile methodologies.· Completed an advanced Data Science program, focusing on Data Manipulation, Visualization, Machine Learning, Python, SQL, and cloud platforms like AWS and Azure.· Utilized Python libraries (Pandas, NumPy, Matplotlib, scikit-learn) to develop and fine-tune machine learning models, processing complex data formats like JSON and CSV.· Performed sentiment analysis and trend detection using NLP techniques with Python’s NLTK and spaCy libraries.· Collaborated with senior data scientists to uncover patterns in large datasets, applying statistical methods and machine learning algorithms like XGBoost and decision trees.· Enhanced data visualization and analytics using Python and R, creating advanced graphical representations and interactive dashboards with R Shiny.· Implemented predictive models for customer behavior analysis, employing time series analysis and machine learning techniques in R and Python.· Applied Generalized Additive Models (GAM) for user segmentation and preference prediction, focusing on non-linear relationships in large-scale data.· Conducted data cleaning and preprocessing, utilizing advanced techniques for managing missing data, such as multiple imputation.· Developed and validated predictive models, including neural networks and deep learning frameworks like TensorFlow and PyTorch.· Improved model performance through ensemble methods like stacking and boosting, using libraries like LightGBM and CatBoost.· Presented insights to senior management using Power BI, Tableau, and Google Data Studio, driving data-driven decision-making.· Proficient in Big Data technologies like Apache Hadoop and Hive for managing and analyzing large datasets in distributed environments.· Continuously updated technical skills, adopting new technologies to stay ahead in the evolving field of Data Science and Machine Learning. Show less

• Developed data mapping, governance, and transformation rules for MDM architecture involving OLTP, ODS, and OLAP.• Provided source to target mappings for ETL team, enabling initial, full, and incremental loads into the data mart.• Conducted JAD sessions, collected requirements, and defined source to target data mappings and business rules.