Lamiya A.

👋 Welcome to my profile! I’m a Data Scientist and Machine Learning Engineer with 2+ years of experience across the banking, insurance, and procurement sectors, specializing in 𝗖𝗿𝗲𝗱𝗶𝘁 𝗥𝗶𝘀𝗸 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴, 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 (𝗚𝗟𝗠), 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻, 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺𝘀, 𝗮𝗻𝗱 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 (𝗡𝗟𝗣). I help businesses build AI-powered solutions that improve decision-making—from assessing customer creditworthiness and optimizing insurance pricing to automating claim processing, analyzing text and audio data, and developing recommendation systems that enhance customer engagement and operational efficiency. Notable Projects: 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗖𝗹𝗮𝗶𝗺 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 – Built an end-to-end AI platform for vehicle insurance claims using Ovis Vision-Language Model (VLM) for document detection and Google Gemini for vehicle damage assessment, damaged part identification, and severity estimation. Developed an AI-powered pricing engine based on historical part prices and built a scalable architecture using 𝗥𝗮𝗯𝗯𝗶𝘁𝗠𝗤, 𝗠𝗶𝗻𝗜𝗢, 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟, 𝗗𝗮𝘁𝗮𝗶𝗸𝘂 𝗗𝗦𝗦, 𝗗𝗮𝘁𝗮𝗶𝗸𝘂 𝗪𝗲𝗯𝗔𝗽𝗽𝘀, 𝗮𝗻𝗱 𝗚𝗶𝘁 to automate the entire claim processing workflow. 𝗚𝗟𝗠-𝗕𝗮𝘀𝗲𝗱 𝗖𝗔𝗦𝗖𝗢 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹– Developed Generalized Linear Models (GLM) for CASCO insurance pricing by modeling claim frequency and severity using historical portfolio data. Performed feature engineering, variable selection, model validation, and pricing optimization to support data-driven underwriting decisions. Built and deployed 𝗖𝗿𝗲𝗱𝗶𝘁 𝗟𝗶𝗺𝗶𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗚𝗶𝗻𝗶: 𝟳𝟱%–𝟳𝟴%) for cardholder and non-cardholder customers to optimize credit limit decisions. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝗮𝗻 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 (𝟲𝟬% 𝗚𝗶𝗻𝗶) 𝗮𝗻𝗱 𝗮 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗘𝘅𝗶𝘀𝘁𝗲𝗻𝗰𝗲 𝗠𝗼𝗱𝗲𝗹 (𝟳𝟮% 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆) for customer credit risk evaluation. Built an 𝗔𝘂𝗱𝗶𝗼 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗮𝗰𝗵𝗶𝗲𝘃𝗲𝗱 𝗮 𝟰𝟬% 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁, enabling more accurate customer interaction analysis. Developed 𝗦𝘂𝗽𝗽𝗹𝗶𝗲𝗿 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝘂𝘀𝗶𝗻𝗴 𝗟𝗶𝗴𝗵𝘁𝗙𝗠 𝗮𝗻𝗱 𝗡𝗟𝗣 to improve supplier matching and procurement efficiency. Area of Expertise: 𝗣𝘆𝘁𝗵𝗼𝗻 (𝗡𝘂𝗺𝗣𝘆, 𝗣𝗮𝗻𝗱𝗮𝘀, 𝗦𝗰𝗶𝗸𝗶𝘁-𝗹𝗲𝗮𝗿𝗻, 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄, 𝗣𝘆𝗧𝗼𝗿𝗰𝗵, 𝗠𝗮𝘁𝗽𝗹𝗼𝘁𝗹𝗶𝗯, 𝗣𝗹𝗼𝘁𝗹𝘆, 𝗦𝗲𝗮𝗯𝗼𝗿𝗻, 𝗞𝗲𝗿𝗮𝘀) 𝗦𝗤𝗟 (𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗤𝗟 𝗦𝗲𝗿𝘃𝗲𝗿, 𝗢𝗿𝗮𝗰𝗹𝗲 𝗦𝗤𝗟, 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟, 𝗚𝗿𝗲𝗲𝗻𝗽𝗹𝘂𝗺) 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻, 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁, 𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴, 𝗦𝗩𝗠) 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 (𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗟𝗶𝗻𝗲𝗮𝗿 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗚𝗟𝗠), 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴) 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 (𝗩𝗶𝘀𝗶𝗼𝗻-𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀, 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻, 𝗜𝗺𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴, 𝗩𝗲𝗵𝗶𝗰𝗹𝗲 𝗗𝗮𝗺𝗮𝗴𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻) 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 & 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗔𝗜 (𝗚𝗼𝗼𝗴𝗹𝗲 𝗚𝗲𝗺𝗶𝗻𝗶, 𝗢𝘃𝗶𝘀 𝗩𝗟𝗠, 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴) 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 (𝗧𝗲𝘅𝘁 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗔𝘂𝗱𝗶𝗼 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀, 𝗡𝗟𝗧𝗞, 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀) Credit Risk Modeling (Application Scoring, Probability of Default, Limit Management) Insurance Analytics (CASCO Pricing, Claim Frequency & Severity Modeling, Pricing Optimization) Recommendation Systems (LightFM, Collaborative Filtering) Feature Engineering, Feature Selection & Data Mining Data Analysis & Visualization (Matplotlib, Seaborn, Plotly, Excel) Data Extraction & Web Scraping (Beautiful Soup) Model Deployment & AI Infrastructure 𝗥𝗮𝗯𝗯𝗶𝘁𝗠𝗤 𝗠𝗶𝗻𝗜𝗢 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜𝘀 𝗝𝘂𝗽𝘆𝘁𝗲𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸 Education: B.Sc. in Computer Science Keywords: #DataScience #MachineLearning #DeepLearning #ArtificialIntelligence #ComputerVision #VisionLanguageModel #Ovis #Gemini #GenerativeAI #MultimodalAI #InsuranceAI #InsurancePricing #GLM #GeneralizedLinearModel #CASCOPricing #ClaimSeverity #ClaimFrequency #CreditRisk #CreditRiskModeling #ProbabilityOfDefault #LimitManagement #ApplicationScoring #FraudDetection #RiskAnalytics #RecommendationSystem #LightFM #CollaborativeFiltering #NaturalLanguageProcessing #SentimentAnalysis #Python #SQL #PostgreSQL #RabbitMQ #MinIO #Dataiku #TensorFlow #PyTorch #ScikitLearn #FeatureEngineering #PredictiveModeling #ModelDeployment #Automation #JupyterNotebook #BeautifulSoup