Led the end-to-end execution of data science projects, ensuring a seamless journey from data acquisition to real-time visualization.Implemented live CI pipelines, showcasing expertise in integrating, testing, and validating code using DevOps principles.Demonstrated the real-time application of Rust for parallel computing, notably improving the efficiency of machine learning algorithms.Proactively addressed imbalanced fraud datasets using advanced sampling techniques with Python Scikit-learn in real-world situations.Conducted live manipulation of datasets using Python (NumPy, Pandas, Matplotlib) and R, showcasing efficient real-time analysis.Integrated security seamlessly into live DevOps workflows, demonstrating the use of AWS boto3 API for real-time AWS calls.Implemented robust monitoring and logging solutions in real-time for enhanced visibility within DevOps workflows.Utilized Kore.ai's voice integration capabilities to develop voice-enabled bots, providing users with an intuitive and hands-free interaction option.Performed live cleaning and processing of third-party spending data using Excel macros and Python libraries, ensuring real-time deliverables.Executed end-to-end machine learning workflows live, encompassing AWS Snowflake data gathering, preprocessing, modeling, and deployment.Ensured real-time CCPA compliance by hashing sensitive data using AWS Snowflake stored procedures.Created and actively maintained real-time Tableau reports, offering a dynamic display of the status and performance of deployed models and algorithms.Practically worked on NLP for real-time documentation classification, text processing, and summarization using NLTK, SPACY, and TextBlob.Consumed Adobe Analytics web API live and automated real-time data subjects' requests from AWS Snowflake to Adobe Analytics privacy API.Developed and implemented a live integration for HBO subscription data via AWS SQS, highlighting improved data processing speed and reliability.

Proficient in analyzing diverse logs, employing Python libraries to predict future events. Utilized Scikit-Learn's machine learning algorithms for comprehensive data format analysis.Evaluated ANOVA assumptions, ensuring normality and homogeneity of variances, especially in experimental designs like Randomized Controlled Trials (RCTs).Big Data Migration and Processing ,Transformed MapReduce programs into Spark using Scala, showcasing expertise in Python for data analytics, wrangling, and extraction through Pandas, Pyexcel, NumPy, and SciPy.Database Migration and Management .Successfully migrated Django databases from SQLite to MySQL and PostgreSQL, emphasizing data integrity. Employed SQL Alchemy as an ORM mapping tool.Optimized bot performance by leveraging Kore.ai's analytics and reporting features, leading to continuous improvements in user interaction metrics.Integrated Amazon AWS services (S3 and RDS) to host static/media files and databases, focusing on scalability and performance optimization.Software Development and Collaboration: Collaborated across organizational boundaries for innovative software solutions in data science and machine learning.Quality Assurance and Documentation: Implemented robust quality assurance processes, created detailed technical documentation, and effectively communicated complex concepts to diverse stakeholders.Strategic Data Science Leadership: Applied strategic thinking to align data science projects with broader business objectives, contributing to organizational strategy. Stayed current with industry trends.Git Version Control and DevOps: Established Git version control on Atlassian Bitbucket and local environments, emphasizing the importance of code management and collaboration.Scalable Hadoop Cluster Deployment: Deployed scalable Hadoop clusters on AWS with S3 as the underlying file system, showcasing proficiency in big data technologies.

Adept at gathering, analyzing, and translating application requirements into data models. Advocate for standardization and adoption of practices related to data and applications.Applied the Recency, Frequency, and Monetary Value (RFM) methodology to analyze customer behavior. Employed an XGBoost classifier to classify lifetime values, enhancing customer insights.Effective K-means Clustering: Employed the Elbow method for optimal cluster selection in K-means clustering. Applied this technique to recency, frequency, and revenue scores, contributing to insightful customer segmentation.Configuration of Hadoop Tools: Configured various Hadoop tools such as Hive, Pig, Zookeeper, Flume, Impala, and Sqoop for efficient data processing and analysis.Collaborative Data Acquisition: Worked alongside the Data Engineer team to acquire historical and real-time data using Sqoop, Pig, Flume, Hive, MapReduce, and HDFS.Application of Clustering Algorithms , Utilized Scikit and Scipy to implement clustering algorithms like Hierarchical and K-means, enriching understanding and segmentation of customer behavior.Comprehensive Data Management: Executed thorough data collection, cleaning, visualization, and feature engineering using Python libraries including Pandas, Numpy, Matplotlib, and Seaborn.SQL Optimization and Transformation: Optimized SQL queries for transforming raw data into MySQL using Informatica, ensuring structured data readiness for machine learning.Leveraged Tableau for data visualization and interactive statistical analysis, delivering compelling preliminary reports to stakeholders.Effective Collaboration with Business Analysts: Collaborated closely with Business Analysts, contributing insights to understand user requirements and enhancing the design and layout of interactive dashboards.Conducted extensive EDA to study customer behavior, implementing k-means clustering to segment customers based on RFM scores.

Market Analysis Achievements: Successfully met objectives with a 17% portfolio increase and a 6% expansion in the customer base, showcasing efficiency in market analysis.Conducted thorough model evaluations using K-fold cross-validation, generated ROC curves and PR curves for comparison, and analyzed feature importance to identify key factors influencing prediction results.Demonstrated expertise in managing diverse data sets, varying in size and complexity, encompassing both structured and unstructured data.Data Quality Assurance: Maintained high data quality, consistency, and integrity through dedicated data cleaning practices using Pandas and NumPy.Strategic SQL-based Data Manipulation: Implemented strategic data acquisition and manipulation using SQL, contributing to streamlined and efficient data processes.Advanced Financial Time Series Analysis: Applied sophisticated financial time series analytical techniques in Python, including ARIMA, Garch, Exponential Smooth, and Markov Chain.Specialized in implementing effective fraud detection techniques in Python, showcasing proficiency with highly unbalanced datasets.Extracted and analyzed transaction data from 11 significant territories (1 million+) using PySpark, achieving a 95% accuracy rate in forecasting areas with higher revenue using SkLearn/MLLib.Insights-Driven Optimization: Identified trends and insights, optimizing spend and performance based on data-driven insights.Deployed various machine learning models, consistently updating them with quarterly developments and improvements.Revenue Enhancement Strategies: Successfully increased revenue by 10% through the strategic application of Bagging and Boosting algorithms, minimizing False Positive and False Negative rates.Executed crucial data pre-processing tasks, including merging, sorting, finding outliers, missing value imputation, and data normalization, ensuring data readiness for in-depth analysis.