About

Meenu Agarwal is a highly motivated professional with over 3 years of experience in the field of Machine Learning and Data Science. Currently working as a Data Scientist at Societe Generale, Meenu has helped clients solve critical business problems in Retail, CPG, and Ad Tech domains. Her current work involves text analytics and classification, as well as privacy-preserving algorithms and synthetic data generation. Meenu's expertise lies in Deep Learning and Computer Vision techniques for object detection and face recognition. She has published a research paper in the international IEEE conference (IPTA, 2017) on detection of non-helmeted motorcyclists along with license plate extraction. She is highly skilled in Python, SQL, PySpark, and TensorFlow. Previously, Meenu worked with Sigmoid as a Data Scientist II, where she was responsible for data analysis and visualization, predictive modeling using XGBoost for demand forecasting, multi-touch attribution for ad campaign optimization, market basket analysis, and product cycle analysis for organic sales growth. She has also worked with Azure Databricks, Jupyter Notebook, Power BI, and Excel. Meenu holds a Bachelor of Technology in Electronics and Communication Engineering from the National Institute of Technology, Surat. She is proficient in a wide range of technologies, including Neural Networks, XGBoost, AWS, Azure, Jupyter, FaceNet, Databricks, Hyperparameter, OpenCV, Image Processing, Data Engineering, Scikit, and Integration. With her relevant experience of 4.53 years, Meenu is a highly skilled and accomplished Data Scientist who is always keen to take on new challenges and deliver exceptional results.