Data Scientist & Generative AI Engineer

Tanya Walia

Data Scientist Machine Learning Engineer Generative AI Engineer

USA +1-410-***-**** Email: *****@***********.*** LinkedIn Github SUMMARY

Data Scientist with 3+ years of experience building and deploying machine learning, NLP, and Generative AI solutions across insurance, healthcare, and enterprise analytics domains. Experienced in predictive modeling, statistical analysis, NLP, customer analytics, risk analytics, and Generative AI applications including LLMs, RAG pipelines, prompt engineering, and model evaluation. Proven track record of delivering scalable data products and AI-driven solutions that improve business performance and operational efficiency. TECHNICAL SKILLS

• Programming: Python, SQL, R, Pandas, NumPy, PySpark

• Machine Learning: Scikit-learn, XGBoost, Random Forest, Classification, Regression, Clustering, Feature Engineering, A/B Testing, Statistical Modeling, Time Series Forecasting

• Deep Learning & NLP: PyTorch, Hugging Face Transformers, NLP, Transfer Learning, Fine-Tuning

• Generative AI: GPT-4, Claude, LLaMA, LangChain, LlamaIndex, RAG, Prompt Engineering, LoRA/PEFT, Vector Databases (FAISS, ChromaDB)

• MLOps: MLflow, Docker, Kubernetes, Airflow, Git, CI/CD, Model Monitoring

• Data Engineering: Apache Spark, Databricks, Snowflake, ETL Pipelines

• Cloud: AWS (S3, EC2), Azure, GCP

• Visualization: Power BI, Tableau

PROFESSIONAL EXPERIENCE

Data Scientist (AI) - Wells fargo USA Sep 2025 – Present

• Developed and deployed machine learning models using Python, PySpark, and Scikit-learn to support customer analytics, risk assessment, and operational decision-making, reducing model development cycles by 25%.

• Built and optimized large-scale data pipelines using Apache Spark, Databricks, and SQL to process structured and unstructured datasets, improving data availability and reducing analytics processing latency.

• Designed predictive analytics solutions for customer behavior modeling, segmentation, and business performance measurement, enabling data-driven decision making across multiple teams.

• Engineered Retrieval-Augmented Generation (RAG) applications using GPT-4, LangChain, and vector databases to enhance enterprise knowledge search and internal productivity workflows.

• Developed model monitoring and evaluation frameworks using MLflow to track model accuracy, drift, latency, and operational performance in production environments.

• Deployed containerized machine learning and AI services using Docker, Kubernetes, AWS, and CI/CD pipelines, improving scalability and reliability of production systems.

• Collaborated with business, engineering, and analytics teams to implement AI-powered workflow automation solutions and conducted A/B testing that improved model performance by 18%.

Data Scientist Intern - Athena Enzyme Systems USA Apr 2025 – Aug 2025

• Supported the development of machine learning and Generative AI solutions for document intelligence, knowledge retrieval, and business process automation.

• Built a Retrieval-Augmented Generation (RAG) pipeline using LangChain and ChromaDB to index and retrieve information from 1,000+ domain documents, reducing manual validation effort by 75%.

• Assisted in fine-tuning and evaluating open-source LLMs using LoRA and PEFT techniques for domain-specific question-answering applications.

• Developed automated evaluation workflows using Python and MLflow to measure response quality, relevance, and consistency across AI applications.

• Collaborated with cross-functional teams to deploy and monitor AI solutions, supporting reliable model iteration and production readiness. Data Scientist - Allstate Insurance India Feb 2022 – Jul 2024

• Developed and deployed machine learning and NLP solutions supporting customer analytics, service operations, and large-scale production data pipelines across millions of users.

• Built multi-class classification models for ticket routing and customer support automation across 1M+ annual interactions, reducing resolution time by 30%.

• Developed NLP pipelines for customer feedback analysis and sentiment classification, improving prediction accuracy by 20% and supporting product improvement initiatives.

• Engineered Spark, PySpark, and Snowflake ETL pipelines on Databricks and AWS infrastructure, processing multi-million-record datasets while reducing reporting latency by 22%.

• Conducted statistical analysis, hypothesis testing, and customer behavior analytics to support retention, marketing, and operational strategy initiatives.

• Collaborated with business stakeholders to develop reporting dashboards and data-driven insights using SQL, Tableau, and Power BI. PROJECT

Enterprise RAG Knowledge Assistant Tech: Python, GPT-4, Claude, LangChain, LlamaIndex, ChromaDB, FAISS, Docker

• Built an enterprise Retrieval-Augmented Generation (RAG) platform using LangChain, LlamaIndex, and vector databases to enable semantic search across 10,000+ documents while reducing hallucinations by 45%.

• Developed automated LLM evaluation pipelines for relevance, groundedness, latency, and response quality, improving AI response accuracy and production reliability.

LLM Evaluation & AI Governance Framework Tech: Python, GPT-4, Claude, MLflow, Docker, LangSmith

• Designed an AI governance and evaluation framework for LLM applications, implementing prompt regression testing, hallucination detection, and policy compliance validation.

• Built MLflow-based monitoring dashboards tracking model quality, latency, and safety metrics, reducing AI deployment failures by 35%. EDUCATION

M.S. Data Science University of Maryland, Baltimore County Aug 2024 – May 2026 B.Tech Computer Science University of Petroleum & Energy Studies Aug 2018 – May 2022

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