Intern – Data Analytics (Radar and Flight) – Red Balloon Aerospace

About Red Balloon Aerospace

Flagship Technology Stack

• VISTA: Super-pressure stratospheric balloons operating at 20–50 km for persistent Earth observation and communication relays.
• ALTIS: Long-endurance tethered aerostats engineered for border surveillance, maritime domain awareness, and continuous data relay.
• HELIX: Hydrogen-elevated cargo airships designed for heavy-lift long-range zero-emission logistics (up to 20-ton payload capacity).
• DIVE (Data Inference & Visualization Engine): The proprietary core engine turning sensor telemetry into actionable intelligence for defense, climate, and infrastructure sectors.

Role Overview

Key Responsibilities

Data Analysis & Signal Processing

• Multi-Sensor Telemetry Analysis: Clean, filter, and process raw time-series telemetry streams (GPS, Inertial Measurement Unit, barometer, and magnetometer).
• Radar Target Tracking: Process radar datasets to support target detection, tracking, clutter filtering, and target classification.
• Algorithm Engineering: Implement digital signal filtering, noise reduction algorithms, and feature extraction pipelines in Python.
• Flight Dashboards: Build visual telemetry monitoring dashboards in Tableau, Power BI, or Matplotlib for flight test evaluations.

Machine Learning & Model Development

• Trajectory & Anomaly Modeling: Construct predictive statistical models for flight trajectory estimation and ML models for sensor anomaly detection.
• Digital Twin Support: Assist engineering teams in training data-driven digital twin models for tethered aerostats (ALTIS) and balloon platforms (VISTA).

Reporting & Cross-Functional Teamwork

• Documentation & Verification: Document mathematical methodologies, noise filter parameters, and model validation outcomes.
• Engineering Briefings: Present analytical findings and algorithm performance metrics to senior avionics and AI flight leads.

Technical & Eligibility Requirements

• Education: Pursuing or completed B.Tech / M.Tech in Data Science, Computer Science, Aerospace Engineering, Signal Processing, or a related quantitative field.
• Core Programming: High proficiency in Python and its scientific computing library stack (NumPy, Pandas, Matplotlib, Scikit-learn).
• Domain Foundations: Basic understanding of digital signal processing (DSP), time-series analysis, and sensor fusion basics.
• Preferred Experience:
  • Familiarity with deep learning frameworks (TensorFlow, PyTorch).
  • Hands-on knowledge of aerospace systems, IMU telemetry, radar data processing, or Kalman filtering.
  • Experience with BI dashboards (Power BI, Tableau).

Role Summary