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Data Scientist - Geospatial Data Analysis

Data Scientist - Geospatial Data Analysis

first career centreBangalore
23 days ago
Job description

Position : Data Scientist ML & Geospatial Specialist

About the Role :

We are seeking a Data Scientist with expertise in Machine Learning (ML), Deep Learning, and Geospatial Data Analysis to develop predictive models, deploy scalable AI solutions, and derive insights from geospatial datasets (e.g., GIS, Google Maps, ArcGIS). The ideal candidate will have strong programming skills in Python, experience with FastAPI for model deployment, and familiarity with Azure Cloud. Knowledge of Generative AI and NLP is a plus.

Key Responsibilities :

  • Design, train, and optimize Machine Learning and Deep Learning models for predictive analytics.
  • Process and analyze geospatial data (GIS, Google Maps, ArcGIS) for demand pattern analysis, location intelligence, and spatial modeling.
  • Develop RESTful APIs (using FastAPI) to deploy ML models into production.
  • Implement end-to-end ML pipelines - from data preprocessing to model deployment on Azure (e.g., Azure ML, Databricks).
  • Collaborate with teams to integrate geospatial insights into business solutions (e.g., logistics, urban planning, retail demand forecasting).
  • Apply Generative AI / NLP techniques (optional) for text / geo-data enhancements (e.g., geocoding, sentiment analysis of location-based reviews).
  • Perform time-series forecasting and clustering for demand / supply pattern analysis.
  • Visualize geospatial data and model outputs using Python libraries (Folium, Geopandas) or tools like Tableau.
  • Ensure scalability and monitoring of deployed models (MLOps).

Mandatory Skills :

  • Programming : Expert in Python (Pandas, NumPy, Scikit-learn, TensorFlow / PyTorch).
  • ML / DL : Strong background in supervised / unsupervised learning, neural networks, and ensemble methods.
  • Geospatial Analytics : Hands-on experience with GIS data, Google Maps API, ArcGIS, or similar tools.
  • Model Deployment : Proficiency in FastAPI, Flask, or Django for API development.
  • Cloud Platforms : Experience with Azure (Azure ML, AKS, Blob Storage).
  • Data Engineering : SQL and big data tools (e.g., Spark) for geospatial data processing.
  • ref : hirist.tech)

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