I am a dedicated Spatial Modeller at UKCEH with a robust foundation in GIS and urban data science. I excel in transforming complex spatial data into clear, strategic narratives with extensive experience in modelling. My proven expertise in Python, R, SQL, and a suite of GIS platforms empowers me to drive impactful decisions, optimize data visualizations, and communicate sophisticated analyses to diverse stakeholder groups.
Carbon Model: Employed Random Forest regression to train the carbon model to predict above ground biomass, with GEDI biomass as the dependent variable, achieving an R² value of 77% accuracy.
• Tools: Google Earth Engine, Python, QGIS
(Output: https://projects-pond.d12vezg9i9v6xv.amplifyapp.com/)
GIS Analyst (Marron NYU) (02/2017 – 1/2020)
Programming & Technical Proficiencies
Python (Pandas, NumPy, Scikit‑Learn), R, SQL
GIS platforms including ArcGIS, QGIS, GEE
Database management with PostgreSQL and PostGIS
Familiarity with version control using Git
Data Analytics & Machine Learning
Predictive modeling, and time series forecasting
Machine learning techniques (Random Forest regression, unsupervised learning, sentiment analysis)
Data Visualisation & Reporting
Expertise in developing dynamic visualisations and dashboards using Tableau and custom Python tools
Professional & Soft Skills
Technical leadership, project management, and effective stakeholder engagement
Strong written and oral communication, strategic planning, and problem-solving abilities
Adept at multitasking, documentation, and delivering clear, impactful presentations
Analyzing Trends and Forecasting Road Accident Severity in the UK
Planning Application Report Using Gen AI:
Consumer Segmentation and Catchment Area Analysis (Dissertation- Northern Rail)
Selected as a mentee in the Data Visualisation Society Mentorship Program, led by Michail, to develop skills in business-oriented data storytelling using Tableau.
English(Fluent), Hindi(Native), Marathi(Native), Arabic(Intermediate)