Vaibhav Saraf

1. Developed and optimized scalable data pipelines (ETL/ELT) for processing large, complex datasets, supporting decision-making and improving business outcomes.
2. Implemented prescriptive and predictive modeling techniques, providing actionable insights via BI platforms and collaborating with teams to align data strategies with business needs.
3. Built and integrated data pipelines using Apache Spark, Azure Data Factory, PySpark, and Azure Databricks, ensuring efficient data flow and reduced processing time.
4. Automated data processing workflows and managed DevOps pipelines in an Agile environment, ensuring timely delivery of data science solutions and reliable data integration.
5. Ensured data quality and integrity through data validation checks, continuous monitoring, and working with cloud-based infrastructure (Azure) to optimize system scalability and performance.

1. Gathered requirements from stakeholders to design data models and reports, leveraging Azure Databricks for efficient data processing and model execution.
2. Analyzed large datasets to identify trends, patterns, and correlations for business insights, utilizing Azure Databricks to scale computations and optimize workflows.
3. Translated raw data into meaningful information using statistical techniques and machine learning models, executed and optimized in Azure Databricks.
4. Created predictive models to forecast customer behavior, using Azure Databricks for model development, training, and deployment.
5. Automated manual reporting processes with Python scripts, integrated with Azure Databricks to ensure seamless data integration and reporting.

1. Developed and maintained data analysis reports to identify trends, patterns, and correlations, leveraging Azure Databricks for scalable data processing and insights generation.
2. Designed interactive dashboards and visualizations to display insights from data analysis projects, integrated with Azure Databricks for real-time data updates.
3. Analyzed large datasets using advanced analytical techniques, including machine learning algorithms, running computations efficiently in Azure Databricks to improve processing speed and model performance.
4. Managed diverse projects for data capture, storage, and forecast analysis, optimizing workflows with Azure Databricks to ensure smooth data integration and timely insights.

• Gathered requirements from stakeholders to design data models and reports.
• Translated raw data into meaningful information using statistical techniques.
• Conducted exploratory analysis of complex datasets using Python libraries such as NumPy and Pandas.

• Predictive analytics for fixed asset valuation using different statistical and machine learning methods for banking industry clients such as HSBC, Edelweiss, Bank of Baroda, and Citi Bank.
• Reviewed legal documents and records to assess accuracy and compliance with applicable laws, regulations, and standards.
• Conducted research into industry-specific market conditions and competitor analysis.