Geospatial Data Scientist

Role Overview: You will be a Data Scientist with a strong foundation in signal processing, sensor fusion, and machine learning for hardware-integrated environments. Your role will involve collaborating closely with quantum sensing, drone engineering, and embedded systems teams to analyze and model multi-sensor datasets including magnetometer, IMU, and UAV telemetry data. The objective is to develop robust navigation, localization, and anomaly detection systems capable of operating in GPS-denied environments through advanced sensor fusion and physics-informed AI models.

Key Responsibilities: - Design scalable data ingestion, preprocessing, and cleaning pipelines for magnetometer, IMU, and UAV telemetry streams. - Develop physics-informed machine learning models for trajectory estimation, drift correction, and anomaly detection. - Implement sensor fusion algorithms including Kalman Filters, Particle Filters, and deep learning architectures (LSTM, Transformer, Graph-based models). - Integrate magnetic, inertial, and positional data to enable robust localization in GPS-denied environments. - Apply signal processing techniques to extract meaningful patterns from noisy sensor data.

- Generate magnetic anomaly maps and geospatial heatmaps from airborne and terrestrial survey datasets. - Perform statistical analysis, clustering, and signal denoising to identify spatial and temporal patterns. - Build internal visualization tools and dashboards for real-time monitoring, data interpretation, and mission replay.

- Work closely with hardware and firmware engineers to define data interfaces and synchronization standards for live acquisition. - Participate in field testing, calibration campaigns, and post-mission validation of collected sensor data. - Document algorithms, experimental results, and contribute to technical proposals, research publications, and intellectual property filings.

Qualification Required: - B.Tech / M.Tech / Ph.D. in Data Science, Physics, Electrical Engineering, Computer Engineering, Robotics, or Applied Mathematics. - 28 years of experience in multi-sensor data analytics, robotics, autonomous systems, or scientific machine learning. - Strong programming skills in Python, including experience with NumPy, Pandas, PyTorch/TensorFlow, and Scikit-learn. - Solid understanding of signal processing techniques (Fourier transforms, wavelet analysis, filtering, and noise modeling). - Experience working with time-series data and probabilistic modeling. - Familiarity with Kalman filtering, Bayesian estimation, and sensor fusion algorithms. - Experience with geospatial data tools such as GeoPandas, QGIS, or rasterio.

Additional Details of the Company: You will have the opportunity to work on next-generation sensing and navigation technologies combining AI and advanced hardware systems. You will collaborate with a multidisciplinary R&D team including physicists, engineers, and AI researchers. Access to high-fidelity sensor datasets and advanced hardware platforms will be provided along with competitive compensation, performance incentives, and opportunities for intellectual property participation. Role Overview: You will be a Data Scientist with a strong foundation in signal processing, sensor fusion, and machine learning for hardware-integrated environments. Your role will involve collaborating closely with quantum sensing, drone engineering, and embedded systems teams to analyze and model multi-sensor datasets including magnetometer, IMU, and UAV telemetry data. The objective is to develop robust navigation, localization, and anomaly detection systems capable of operating in GPS-denied environments through advanced sensor fusion and physics-informed AI models.

Key Responsibilities: - Design scalable data ingestion, preprocessing, and cleaning pipelines for magnetometer, IMU, and UAV telemetry streams. - Develop physics-informed machine learning models for trajectory estimation, drift correction, and anomaly detection. - Implement sensor fusion algorithms including Kalman Filters, Particle Filters, and deep learning architectures (LSTM, Transformer, Graph-based models). - Integrate magnetic, inertial, and positional data to enable robust localization in GPS-denied environments. - Apply signal processing techniques to extract meaningful patterns from noisy sensor data.

- Generate magnetic anomaly maps and geospatial heatmaps from airborne and terrestrial survey datasets. - Perform statistical analysis, clustering, and signal denoising to identify spatial and temporal patterns. - Build internal visualization tools and dashboards for real-time monitoring, data interpretation, and mission replay.

- Work closely with hardware and firmware engineers to define data interfaces and synchronization standards for live acquisition. - Participate in field testing, calibration campaigns, and post-mission validation of collected sensor data. - Document algorithms, experimental results, and contribute to technical pr