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Data professional with about 6 years of experience delivering end-to-end analytics and predictive modeling solutions—from raw data to analysis-ready datasets. Comfortable with SQL and Python daily, building feature engineering workflows, evaluating models, and processing large-scale data with Apache Spark/PySpark across Databricks, Snowflake, and Teradata, while maintaining ETL pipelines that power downstream reporting. I also explore Generative AI and LLM use cases such as prompt engineering, retrieval-augmented generation (RAG), NLP-based information extraction, semantic search, and summarization. I enjoy translating business questions into actionable data products and presenting results through dashboards in Tableau and Power BI, partnering closely with engineering and business stakeholders to build reliable, production-minded solutions.…Data professional with about 6 years of experience delivering end-to-end analytics and predictive modeling solutions—from raw data to analysis-ready datasets. Comfortable with SQL and Python daily, building feature engineering workflows, evaluating models, and processing large-scale data with Apache Spark/PySpark across Databricks, Snowflake, and Teradata, while maintaining ETL pipelines that power downstream reporting. I also explore Generative AI and LLM use cases such as prompt engineering, retrieval-augmented generation (RAG), NLP-based information extraction, semantic search, and summarization. I enjoy translating business questions into actionable data products and presenting results through dashboards in Tableau and Power BI, partnering closely with engineering and business stakeholders to build reliable, production-minded solutions.

sai divya

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Data professional with about 6 years of experience delivering end-to-end analytics and predictive modeling solutions—from raw data to analysis-ready datasets. Comfortable with SQL and Python daily, building feature engineering workflows, evaluating models, and processing large-scale data with Apache Spark/PySpark across Databricks, Snowflake, and Teradata, while maintaining ETL pipelines that power downstream reporting. I also explore Generative AI and LLM use cases such as prompt engineering, retrieval-augmented generation (RAG), NLP-based information extraction, semantic search, and summarization. I enjoy translating business questions into actionable data products and presenting results through dashboards in Tableau and Power BI, partnering closely with engineering and business stakeholders to build reliable, production-minded solutions.…Data professional with about 6 years of experience delivering end-to-end analytics and predictive modeling solutions—from raw data to analysis-ready datasets. Comfortable with SQL and Python daily, building feature engineering workflows, evaluating models, and processing large-scale data with Apache Spark/PySpark across Databricks, Snowflake, and Teradata, while maintaining ETL pipelines that power downstream reporting. I also explore Generative AI and LLM use cases such as prompt engineering, retrieval-augmented generation (RAG), NLP-based information extraction, semantic search, and summarization. I enjoy translating business questions into actionable data products and presenting results through dashboards in Tableau and Power BI, partnering closely with engineering and business stakeholders to build reliable, production-minded solutions.

Available to hire

Data professional with about 6 years of experience delivering end-to-end analytics and predictive modeling solutions—from raw data to analysis-ready datasets. Comfortable with SQL and Python daily, building feature engineering workflows, evaluating models, and processing large-scale data with Apache Spark/PySpark across Databricks, Snowflake, and Teradata, while maintaining ETL pipelines that power downstream reporting.

I also explore Generative AI and LLM use cases such as prompt engineering, retrieval-augmented generation (RAG), NLP-based information extraction, semantic search, and summarization. I enjoy translating business questions into actionable data products and presenting results through dashboards in Tableau and Power BI, partnering closely with engineering and business stakeholders to build reliable, production-minded solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

Data Scientist at Southwest Airlines

February 1, 2025 - Present

Built SQL and Python-based data pipelines to convert raw operational data into structured datasets for reporting, analytics, and ML workflows. Performed EDA and statistical validation to detect patterns, anomalies, and data-quality issues. Applied machine learning for predictive analytics, classification, forecasting, and pattern identification. Developed feature engineering workflows in Python and SQL for multi-source datasets used by ML models, including dataset preparation and model evaluation. Explored NLP/LLM approaches for unstructured text tasks using prompt engineering and RAG-style information extraction and semantic retrieval. Monitored pipeline health and investigated data-quality issues impacting downstream analytics and ML. Queried and joined data across Databricks, Snowflake, and Teradata, and collaborated with Finance, Operations, and Product teams to translate business needs into data/AI requirements. Documented data definitions, transformation logic, analytical assumpt

Data Scientist at CVS– NC, USA

January 1, 2024 - December 31, 2024

Applied statistical analysis and machine learning to identify patterns, relationships, and key drivers in large operational datasets. Built Bayesian Network models to capture direct and indirect variable drivers. Prepared ML datasets using SQL/Python with cleaning, validation, preprocessing, and feature preparation. Performed EDA to identify trends, outliers, correlations, and data-quality issues prior to modeling. Created ETL workflows in Databricks to consolidate multi-source data into analysis-ready datasets. Supported ML experimentation by comparing approaches and reviewing model inputs/outputs. Explored GenAI/LLM concepts including information extraction, summarization, semantic search, and knowledge retrieval, including prompt engineering and structuring source data for downstream retrieval/generation. Presented findings to stakeholders and documented methods, assumptions, and results for reproducibility.

Data Scientist at Capgemini

June 1, 2019 - July 31, 2023

Cleaned, preprocessed, and transformed data using Python (Pandas) and SQL to prepare datasets for analytics and machine learning. Built predictive models with Scikit-learn (Linear Regression, Decision Trees) and performed EDA for anomalies, correlations, distributions, and data quality assessment. Applied feature engineering to transform raw business/customer data into modeling variables. Used Apache Spark and PySpark for distributed processing of large datasets. Built and maintained ETL and data-integration pipelines using Python/SQL and AWS S3 to supply reliable datasets. Produced Tableau dashboards and BigQuery reporting views to communicate operational, sales, and performance metrics. Collaborated with business analysts and reporting teams to translate requirements into data and ML tasks, and supported early AI/ML and GenAI experimentation including text processing for information extraction, summarization, semantic search, and knowledge retrieval.