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Data Scientist with 4+ years of experience building and deploying machine learning and GenAI systems, including LLM-based applications, predictive models, and scalable ML pipelines. Strong focus on improving customer engagement, operational efficiency, and measurable model performance. Skilled in end-to-end ML lifecycle—data ingestion, feature engineering, training, deployment, and monitoring—using AWS, Python/SQL, Spark, and MLOps practices. Experienced in NLP (text classification, sentiment analysis, NER), RAG with OpenAI APIs, and delivering executive-ready dashboards for data-driven decision-making.…Data Scientist with 4+ years of experience building and deploying machine learning and GenAI systems, including LLM-based applications, predictive models, and scalable ML pipelines. Strong focus on improving customer engagement, operational efficiency, and measurable model performance. Skilled in end-to-end ML lifecycle—data ingestion, feature engineering, training, deployment, and monitoring—using AWS, Python/SQL, Spark, and MLOps practices. Experienced in NLP (text classification, sentiment analysis, NER), RAG with OpenAI APIs, and delivering executive-ready dashboards for data-driven decision-making.

Aditi Dilip Pabal

Data Scientist, AI Engineer, Data Engineer, +2

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Data Scientist with 4+ years of experience building and deploying machine learning and GenAI systems, including LLM-based applications, predictive models, and scalable ML pipelines. Strong focus on improving customer engagement, operational efficiency, and measurable model performance. Skilled in end-to-end ML lifecycle—data ingestion, feature engineering, training, deployment, and monitoring—using AWS, Python/SQL, Spark, and MLOps practices. Experienced in NLP (text classification, sentiment analysis, NER), RAG with OpenAI APIs, and delivering executive-ready dashboards for data-driven decision-making.…Data Scientist with 4+ years of experience building and deploying machine learning and GenAI systems, including LLM-based applications, predictive models, and scalable ML pipelines. Strong focus on improving customer engagement, operational efficiency, and measurable model performance. Skilled in end-to-end ML lifecycle—data ingestion, feature engineering, training, deployment, and monitoring—using AWS, Python/SQL, Spark, and MLOps practices. Experienced in NLP (text classification, sentiment analysis, NER), RAG with OpenAI APIs, and delivering executive-ready dashboards for data-driven decision-making.

Available to hire

Data Scientist with 4+ years of experience building and deploying machine learning and GenAI systems, including LLM-based applications, predictive models, and scalable ML pipelines. Strong focus on improving customer engagement, operational efficiency, and measurable model performance.

Skilled in end-to-end ML lifecycle—data ingestion, feature engineering, training, deployment, and monitoring—using AWS, Python/SQL, Spark, and MLOps practices. Experienced in NLP (text classification, sentiment analysis, NER), RAG with OpenAI APIs, and delivering executive-ready dashboards for data-driven decision-making.

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Work Experience

Machine Learning Engineer at Master card

June 1, 2025 - Present

Designed and deployed LLM-based Generative AI solutions using RAG architecture, improving document processing efficiency by 35%. Built scalable ML pipelines on AWS SageMaker, reducing deployment time by 40%. Developed and fine-tuned NLP models for text classification and sentiment analysis (92% accuracy). Implemented real-time data processing pipelines using Apache Spark for large-scale production datasets. Deployed models via REST APIs and applied MLOps practices (CI/CD, monitoring, versioning). Created executive dashboards in Power BI/Tableau to visualize model outputs and business KPIs, reducing analysis turnaround by 30%. Delivered end-to-end ML system architecture across ingestion, feature engineering, training, deployment, and monitoring.

Data Scientist at PWC Consulting

June 1, 2022 - May 31, 2024

Led the end-to-end ML lifecycle: data collection, preprocessing, model development, evaluation, and deployment. Built predictive models to improve customer retention by 20% and recommendation systems increasing engagement by 25%. Optimized models (XGBoost, Random Forest), improving accuracy by 15%. Developed scalable ETL and data pipelines using Python, SQL, and Airflow on Azure and GCP. Designed interactive Power BI/Tableau dashboards, reducing reporting time by 30% and improving stakeholder decision speed. Implemented A/B testing frameworks to evaluate and improve model performance in production.

Jr. Data Scientist at Accenture

June 1, 2021 - June 30, 2022

Engineered SQL queries for data extraction and reporting. Assisted in building and validating machine learning models. Improved data quality by 15% through validation and preprocessing. Built and deployed ML models using statistical and predictive techniques. Performed data cleaning, visualization, and hypothesis testing with Python libraries. Designed and optimized data architectures across relational and NoSQL systems, reducing query latency by 25% and improving analytics reporting efficiency.