Certified data scientist with expertise in Advanced AI and ML, proficient in statistical analysis and machine learning. Proven ability to solve real-world challenges in diverse industries through data-driven decision-mak…
Certified data scientist with expertise in Advanced AI and ML, proficient in statistical analysis and machine learning. Proven ability to solve real-world challenges in diverse industries through data-driven decision-making. Dedicated to driving organizational advancement through data analytics.
Leading Early stage of fraud claim detection. Desined and developed data acquisition, data preprocessing, EDA and feature engineering. This proactive approach resulted in reduced fraudulent claims and more efficient claim process.
Fraud claims prediction and integration of the flagging system in the software.
Objective: Classification of the fraudulent claims and flagging them in the initial stages of claim process.
Developed understanding of business problem and contributed in designing project stages.
Performed Data acquisition, preprocessing, EDA, and feature engineering to derive new features from existing.
Used transfer learning using BERT to extract word vectors for text data i.e., cause of accident.
Extracted the data using SQL and pre-processed the big data using parallel computing systems like Dask.
Trained Logistic regression as base model and finalized Random Forest after comparing other models.
Generalized the model by testing it against the test queries to make the model production ready.
Segmentation of low, medium and high liability claims using clustering.
Objective: Segmenting the claims in low, medium and high liability claims to maintain equal workloads amongst surveyors as well as assigning high liability claims to external surveyors.
Developed understanding of business problem and performed data acquisition, data preprocessing and EDA.
Used domain knowledge and feature engineering techniques to derive the new features from existing.
Used K-means clustering to cluster the claims in low, medium and high liability claims.
Developed a rule-based system with the help of IT team to maintain the workload within surveyors.
Fraud Insurance Claim Prediction and Alerting.
Objective: Classification of the fraudulent claims and integrating it with app that will send notification alert.
Performed data extraction from company’s database using MySQL, stored and maintained updated data.
Performed preprocessing like, removing duplicate and missing value rows etc., using Python and Excel.
Completed basic EDA using Python and Tableau and presented the insights of the data before team.
Performed other supporting tasks to build and productionize the model.
Health Insurance Cross Sell Prediction-ML Classification.
Objective: Prediction of customers whether they interested to purchase insurance policy or not.
Performed data extraction from company’s database using MySQL, stored and maintained updated data.
Performed preprocessing like, removing duplicate and missing value rows etc., using Python and Excel.
Completed basic EDA using Python and Tableau and presented the insights of the data before team.
Performed other supporting tasks to build and productionize the model.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
د.جوب منطقة حرة ذ.م.م. 2026 © جميع الحقوق محفوظة
