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Postdoctoral Researcher @CDS, Boston University Expertise: Remote sensing, Satellite imagery, Computer vision, Deep Learning.Rainfall forecasting
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
https://www.bu.edu/cds-faculty/profile/indrajit-kalita/
Data Scientist
June 19, 2026 – Present
SeasonalRainbelt
February 23, 2026 – Present
MLWAM: Physics-Guided Seasonal Prediction of the West African Monsoon Rainbelt
View ProjectGPM_dataDownloader
May 28, 2025 – May 28, 2025
A simple and easy-to-use script for downloading NASA GPM (Global Precipitation Measurement) data. Perfect for researchers, meteorologists, and data enthusiasts working with rainfall and climate datasets.
View ProjectRainfallForecasting
October 3, 2024 – July 16, 2025
Data-driven Rainfall Prediction at a Regional Scale: A Case Study with Ghana
View ProjectIndian-Crop-Classification-using-UAV-Images
December 26, 2022 – August 6, 2023
Crop classification using aerial images by analyzing an ensemble of DCNNs under multi-filter & multi-scale framework
View ProjectMultiSensor-DA
August 27, 2021 – December 27, 2022
MultiSensorDA Domain adaptation under multi-sensor environment. The work has been submitted for publication. After publication/review all the source codes related to the work will be uploaded here. Currently, this repository contains two datasets: HID (Hyperspectral Image Dataset, filename "HID.zip") and AIMD (Aerial Image Merged Dataset, file name "AIMD.zip"). The HID is obtained by collecting 4 classes from the "Pavia Centre scene" datasets (Link: http://www.ehu.eus/ccwintco/uploads/e/e3/Pavia.mat). Moreover, the AIMD is a combinations of AID and ASCD dataset.
View ProjectCultural Fit Analysis
The candidate's projects are heavily concentrated in environmental science, climate modeling, and remote sensing, which aligns well with a data scientist role in a related domain. However, the lack of diverse project types or team-based work makes it difficult to fully assess broader cultural fit. The single listed 'experience' entry appears to be a link to a faculty profile rather than a traditional employment record, which limits understanding of professional work environment exposure.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions indicate a strong technical focus, but there is no information regarding collaboration, problem-solving approaches, or communication style in a team setting.