Develop and implement algorithms for navigation, positioning, and motion estimation
Work with sensor data (e.g., GPS/GNSS, IMU) to build reliable models
Design and apply sensor fusion techniques to combine data sources
Implement and optimize filtering and estimation algorithms (e.g., Kalman Filters)
Analyze large datasets to identify patterns and performance improvements
Collaborate with engineering teams to integrate models into production systems
Validate models through simulations and real-world data testing
Document methodologies, assumptions, and results for stakeholders

Experience as a Data Scientist, Applied Scientist, or similar role
Strong programming skills in Python
Solid foundation in mathematics, statistics, and linear algebra
Experience working with time-series data and signal processing
Knowledge of estimation and filtering techniques (e.g., Kalman Filters)
Experience with scientific computing libraries (NumPy, Pandas, SciPy)
Understanding of sensor data and real-world systems
Ability to translate mathematical models into production-ready code
Strong problem-solving skills and analytical thinking
Experience in aerospace, robotics, automotive, or navigation systems (Nice-to-Have)

Darwoft

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