Built and integrated solution for advertisers and retailers looking to optimize their media and marketing campaigns by analytics capabilities for campaign efficacy, real-time dashboards for performance monitoring, and quantitative sales impact analysis are just a few of the many benefits that this system will provide. It offers thorough insights into many KPIs, across several channels and platforms, ranging from customer engagement to ROI. The initiative aims to maximize efficiency and return on investment by enabling clients to make data-driven decisions, customize their marketing plans in real-time, and track the effects of these campaigns down to individual sales KPIs.

Roles& Responsibilities:• Utilized Google BigQuery for efficient handling and analysis of large financial datasets related to home loans.• Implemented Google Cloud Storage for secure and scalable data storage solutions.• Developed predictive models using Google AI Platform to forecast loan trends and risks.• Applied Google Data Studio for creating interactive dashboards and reports for data visualization.• Conducted data processing and transformation with Google Cloud Dataflow and Google Cloud Dataprep.• Used Google Cloud Pub/Sub for real-time data streaming and event-driven processing.• Managed project infrastructure and services using Google Cloud Console and Cloud SDK.• Utilize NLP techniques to enhance the conversational experience between customers and the AI-powered contact center.• Develop and fine-tune NLP models for language understanding, sentiment analysis, and intent recognition.• Ensure the AI system can comprehend and respond effectively to a wide range of customer queries and requests.• Manage and deploy the Gen AI contact center experience.• Design and implement machine learning algorithms to improve the Gen AI's ability to provide accurate and context-aware responses.• Continuously monitor and analyze AI performance, identifying areas for improvement and optimization.• Collaborate with data scientists to train and update machine learning models for chatbots and virtual agents.• Ensured data security and compliance with industry standards using Google Cloud IAM and Security Command Center.• Collaborated with team members using Google Workspace tools for effective communication and project management.• Continuously monitored system performance and resource utilization with Google Cloud Operations Suite.Environment: Google BigQuery, Google Cloud Storage, Google AI Platform, Google Data Studio, Google Cloud Dataflow, Google Cloud Dataprep, Google Cloud Pub/Sub, Google Cloud Console, Google Cloud SDK, Google Cloud IAM.

Specialized in finance, I have undertaken numerous challenging projects to leverage data-driven insights for informed decision-making and business growth. One notable project involved developing a cutting-edge machine learning model to predict credit risk, leading to a 7% reduction in default rates and a 5% increase in profitability for the organization. Additionally, I analyzed customer behaviour data to identify cross-selling opportunities, resulting in a remarkable 15% boost in sales and revenue.

Our team focused on cancer research. we collaborated with a group of professionals, including doctors, data scientists, and engineers, to transform and analyse large molecular datasets. Utilizing cloud-based machine learning tools, our work was enabled data analysis and contribute to breakthroughs in cancer treatment. we integrated MRI scans with genetic data, using AI and ML to identify critical correlations. project aimed at revolutionizing cancer diagnostics and treatment.

I have a strong background in financial analysis and data-driven decision-making. My expertise includes developing and maintaining financial models and dashboards using Excel, SQL, and Tableau. I've built data pipelines for accurate financial data extraction and collaborated with cross-functional teams for budgeting and forecasting. I've presented data-driven insights to senior management and implemented solutions for business challenges, utilizing predictive models and KPI tracking. Additionally, I have experience in QA testing and defect tracking using JIRA

To develop an end-to-end predictive maintenance solution for a manufacturing company. Leveraging IoT sensors, we collected real-time data from their industrial equipment and processed it using big data technologies like Hadoop and Spark for efficient analysis. We designed and trained machine learning models to detect potential equipment failures, allowing the company to schedule proactive maintenance, reducing downtime, and improving overall operational efficiency.