Sneha Kulkarni Email & Phone Number

The Colony, TX, US

Mason, Ohio, United States

• Collaborated with business stakeholders to gather and document requirements for change requests raised by various departments, ensuring alignment with business objectives.
• Performed impact analysis to evaluate the effects of change requests on existing systems, processes, and data flows, mitigating potential risks.
• Wrote and executed complex SQL queries for data validation in System Integration Testing (SIT) and User Acceptance Testing (UAT) environments to ensure data accuracy and system integrity.
• Managed and scheduled query execution in testing environments, ensuring timely validation and supporting accurate data transformation and migration processes.
• Created detailed data mapping documents to define source-to-target data flow requirements, ensuring seamless data migration and transformation across systems.
• Maintained comprehensive documentation of data validation processes and results, ensuring future reference for audits and compliance.

Akron, Ohio, United States

• Consolidated contract data from various sources i.e., ERP & software portals using Google Cloud FileStorage and BigQuery. Automated data pipelines identified expiring contracts and spending trends, which led to an 8%.
• Utilized AWS (S3, Athena, EC2) to access and analyze facility supply data from invoices. Employed K-Means clustering to categorize spending and identify cost-saving opportunities. Reduced facility supply costs by 12%.
• Built a custom Kaplan-Meier model from scratch on SageMaker using library scikit-learn to predict claim fraud over time. A serverless data pipeline utilizing AWS Step Functions & Lambda to extract claim data and.

• Calibrated a real-time patient sentiment feedback analysis pipeline, utilizing Apache Kafka for high-throughput data ingestionand Azure Cognitive Services for pre-trained NLP. Processed sentiment classification on over.
• Optimized an Azure-based patient churn prediction system using Azure Event Hubs for real-time ingestion and ApacheHadoop for distributed processing of 10TB of transaction and engagement data. The proactive.
• Developed a scalable patient analytics pipeline on Azure Databricks, utilizing Delta Lake for data consistency across billions ofrecords. Integrated MLflow for streamlined model tracking and periodic retraining, which.
• Built a medication adherence prediction platform using Apache Airflow for data orchestration and PyTorch for training a LoShort-Term Memory (LSTM) within a recurrent neural network model on more than 100 million.
• Augmented the customer-product knowledge graph with Spark SQL and GraphX and implemented a graph collaborative filterialgorithm Node2Vec to increase the recommendations, boosting customer engagement and sales.
• Worked with COBOL and JCL to process legacy data for machine learning models and integrated them into the modern analyticsplatform, enhancing data accessibility and accuracy.

Pittsburgh, Pennsylvania, United States

• Built an Apache Spark ETL pipeline for legal/property data of 800GB. Extracted entities with SpaCy's NER and developed a recommendation engine Matrix Factorization using Spark MLlib, achieving high accuracy, F1-Score.
• Leveraged Azure Synapse Analytics and clustering algorithms - DBSCAN to segment mortgage servicer data, uncovering hidden patterns that led to tailored subservicing strategies. This approach improved an overall.
• Preprocessed customer data with Pandas & Scikit-learn and developed a Cox Proportional Hazards model for CLV prediction, yielding high predictive ratios of 0.74 Harrell's C-index, resulting in building potent customer.

India

• Employed Gaussian Process regression, a non-parametric Bayesian approach, to model the anisotropic trends within wellbore data, improving image quality in depth migration for accurate reservoir characterization.
• Implemented a hybrid demand forecasting model with a team of 3, combining ARIMA - Autoregressive Integrated Moving Average for capturing seasonality and XGBoost for non-linearity patterns within sales data by utilizing.
• Designed and implemented an automated ETL pipeline by utilizing Python framework Kedro, ensuring timely data availability for report generation. Integrated Tableau with python script to create dynamic & interactive.

India

• Collaborated with the client’s supply chain team to identify demand drivers. Built an LightGBM model on SAP S/4HANA data for demand forecasting. Implemented statistical safety stock model - Wilson EOQ for inventory.
• Built end-to-end ML model using AWS SageMaker to automatically classify procurement spend data into predefined categories. This model utilized an algorithm - Support Vector Machine (SVM) to categorize spend based on.

Masters, Computer Science, A

Coursework : Data Mining, Database Systems, Artificial Intelligence, Cloud Computing, Computer , Network & Security, Advanced Operating.

Bachelor Of Engineering - Be, Computer Science