# Mrunali Patil > Data Science @DePaul University | Data Scientist | Research Assistant | Open to opportunities in: AI Engineering • Data Science • Machine Learning Engineering • LLM Development Location: Chicago, Illinois, United States Profile: https://flows.cv/mrunali Curious, driven, and recently graduated, I bridge the gap between cutting-edge AI research and real-world impact. Over the past year, I've applied my skills as a Data Scientist and AI Instructor, leading industry collaborations with teams at Abbott Labs to build everything from content generation systems using GPT-4o to scalable NLP pipelines leveraging web scraping, prompt engineering, and LLM evaluation techniques like RAGAS. My academic background is grounded in machine learning, natural language processing, and data engineering, and I’ve complemented that with hands-on experience deploying solutions that matter — whether it’s guiding students through AI fundamentals at a summer camp or fine-tuning multimodal pipelines that drive content strategy. Currently, I’m exploring opportunities where I can continue building intelligent systems, automate what’s inefficient, and make AI accessible beyond the research lab. Key Skills: LLMs (GPT-4, Claude, Gemini), Prompt Engineering, Python, NLP, Web Scraping, Azure, Streamlit, RAG evaluation, Data Science, AI Pipelines, Content Automation ## Work Experience ### Contingent Worker - AI Engineer @ CCC Intelligent Solutions Jan 2025 – Present | Chicago, IL ### AI Engineer @ Segthlon Jan 2025 – Present | Chicago, IL ### Data Scientist @ DePaul iD Lab Jan 2024 – Jan 2025 | Chicago, IL AI Instructor • Collaborated with Abbott Labs to design and host a Summer AI Camp, educating 11 students AI fundamentals, machine learning, computer vision, and NLP, guiding them in data preprocessing, model tuning, and real-world AI applications. • Developed and deployed an AI-powered content generation system, leveraging Azure GPT-4o, web scraping, and image processing to transform Abbott Labs articles into high-engagement social media posts. Engineered prompt engineering strategies, LLM configurations, and chat history retention to optimize content accuracy and contextual relevance. • Designed AI evaluation metrics using the RAGAS framework, reducing LLM hallucinations and improving content faithfulness. • Engineered a scalable web scraping and AI integration pipeline using Beautiful Soup, regex, and caching techniques to automate structured content extraction. Optimized data processing workflows, enabling seamless text summarization and AI-driven content generation for Abbott Labs. ### Graduate Teaching Assistant @ DePaul University Jan 2024 – Jan 2025 | Chicago, Illinois, United States Teaching Assistant at DePaul University - College of Computing and Digital Media (CDM) -Worked with two CDM professors at DePaul University, mentoring 150+ students in Mining Big Data, refining coursework, grading assignments, and providing detailed feedback to enhance student understanding. -Delivered constructive insights on projects and assignments, helping improve overall class performance by 30% through personalized support and structured evaluations. ### Athletics Broadcast & Media Production Specialist @ DePaul University Athletics Jan 2024 – Jan 2025 | Chicago, Illinois, United States DePaul University Athletics | Lincoln Park / Loop Campus - Live Sports Broadcasting & Technical Support – Assist in producing and directing live broadcasts for DePaul’s 13 varsity sports, managing camera operations, in-broadcast graphics, and game-day technical setups to ensure high-quality sports coverage. - Game Day Production & Coordination – Work closely with Athletics staff and FloSports contacts to execute seamless game-day broadcasts, handling technical logistics and contributing to the overall media production strategy. ### Research Assistant @ DePaul University Jarvis College of Computing and Digital Media Jan 2024 – Jan 2025 | DePaul BioMedicalInformatics Research Lab, Chicago IL • Segregated lung nodule CT scans from the LIDC dataset into four agreement levels (Full, High, Low, No Agreement) and implemented a CNN model, which showed a 47.57% accuracy drop from full to no agreement, quantifying the impact of label uncertainty on deep-learning models. • Trained CNN and Random Forest models on preprocessed CT scan slices, using ResNet18 for deep feature extraction and Random Forest for structured feature analysis. Employed stratified k-fold cross-validation (k=5) to enhance model robustness across varying agreement levels. • Improved model interpretability by implementing advanced machine learning techniques, providing insights that can enhance radiologists' decision-making and increase diagnostic confidence in AI-assisted medical imaging. ### Data Scientist @ Zensar Technologies Jan 2021 – Jan 2023 •Engineered and implemented machine learning pipelines for financial risk scoring using Python, Scikit-learn, XGBoost, enabling same-day risk assessment for 5,000+ client accounts and triggering real-time alerts for high-risk activities. •Programmed AI-powered fraud detection models leveraging PyTorch neural networks, identifying fraud patterns across 8 major transaction types, preventing $1.2M in potential losses annually. •Orchestrated Transformer-based NLP models for analyzing customer complaints and financial documents, automating document categorization and improving processing efficiency. •Implemented credit risk prediction models using ensemble and gradient boosting methods, achieving a 15% reduction in portfolio risk across multiple client accounts. •Architected automated feature engineering pipelines with Python and SQL, generating 120+ derived financial indicators, reducing manual preparation time by 40 hours per month. •Integrated model outputs into real-time dashboards using Power BI, providing predictive insights for 2,000+ accounts daily, improving operational decision-making. •Conducted model validation and back testing using time-series analysis and Monte Carlo simulations, ensuring performance stability across 5 consecutive quarters of financial data. ## Education ### Master's degree in Data Science DePaul University ### Post Graduate Course in Data Science and Machine Learning in Data Science Course Board Infinity ### Bachelor of Science - BS in Information Technology University of Mumbai ### Secondary School Certificate St Thomas English School ## Contact & Social - LinkedIn: https://linkedin.com/in/mrunalipatil2 --- Source: https://flows.cv/mrunali JSON Resume: https://flows.cv/mrunali/resume.json Last updated: 2026-04-18