Mastering Data Magic: My Journey in Statistics, Engineering and Generative AI
CONTACTS
My Audio Resume
A fun podcast on my resume (created using Google Notebook LM)
Achievements
• Successfully designed and implemented advanced Generative AI solutions, including Cloud-based and local LLM solutions, text clustering, summarization, identification of Non-Compliance, and search using embedding techniques.
• Expertly applied various data science algorithms, artificial intelligence, deep learning, natural language processing, and predictive analytics.
• Developed and deployed comprehensive data science solutions from proposal to production, ensuring seamless integration and operational efficiency.
Proficient in multiple programming languages, including Python, MATLAB/Octave, R, C++, VBA, and SQL, demonstrating versatility and technical expertise.
• Skilled in data management, database handling, querying, ETL, and data visualization, providing robust support for data-driven decision-making.
• Experienced in modeling natural resources using statistical and geospatial techniques, resource estimation and simulation
• Analyzed and visualized geographical information (GIS and RS) for natural resource modeling, Demographic parameters, Geospatial prediction
• Led multidisciplinary projects in various roles, including research project management, consulting, teaching, and mentoring, showcasing strong leadership and mentorship skills. Organized several national conferences, scientific committees, and supervised over 10 graduate theses, resulting in multiple scientific publications.
AREAS OF EXPERTISE
Gen-AI, NLP and text analytics: Developing a Mental Health note summarizer on GCP, Worked with Hugging Face Transformers, LangChain, word embedding, Vector representations, network architecture, hyperparameter tuning, text preprocessing, data wrangling/scraping, text clustering and classification, topic modeling, theme exploration, contextual and sentiment analysis (Spacy, NLTK, SBERT,…)
Data Science: Machine learning algorithms and libs (Artificial Intelligence, Deep Learning, Regression, Pattern Recognition, AWS SageMaker, Neural networks and clustering TensorFlow, PyTorch, XGBoost, Keras, Scikit-learn, Statsmodels)
Deep learning, transfer learning, and computer vision: Project experience in generating and analyzing 3D CT-Scan images, using Convolutional Neural Network (CNN), frozen pre-trained models, Open-CV analysis, and VTK visualization
Statistical and Geostatistical modeling: Regression, ANOVA, Time Series, PCA, Clustering, Decision Trees, SVM, Kriging, Variogram and covariance Analysis, Spatial Interpolation, Spatial Autocorrelation, Pattern Analysis.
CX Data Analytics: Understanding CX strategies and Journey Mapping, Data-Driven Analytics, Metrics and Performance measurement
GIS/Geospatial Analysis : Skilled in using ArcGIS, QGIS, and Google Earth Engine geospatial analysis tools and Library (Arcpy, Geopandas, PyProj, ipyleaflet, Folium, leaflet, GDAL, GeoJason).