Senior Data Scientist - Radar and Sensor Analytics

Fortem Technologies•Lindon, UT

About The Position

Fortem Technologies is seeking a Senior Data Scientist to help turn raw radar returns into actionable intelligence. You will initially focus on classifying objects detected by Fortem’s TrueView radar systems — including drones, birds, aircraft, and other airspace traffic — and then expand your work to incorporate drone telemetry and additional sensor data as Fortem’s classification capabilities grow across the SkyDome Family of Systems.

Requirements

  • Master’s or PhD in Computer Science, Data Science, Electrical Engineering, Statistics, or related field, or equivalent experience
  • 5+ years of experience applying machine learning and data science to real-world sensor or time-series data
  • Strong proficiency in Python and standard ML/data libraries (NumPy, pandas, scikit-learn, PyTorch, or TensorFlow)
  • Experience working with radar, RF, or raw sensor data
  • Solid understanding of classification, signal processing, and feature engineering techniques
  • Experience building and maintaining production ML pipelines
  • Strong communication skills, with the ability to translate ML results for a broader engineering audience

Nice To Haves

  • Experience with radar signal processing (micro-Doppler, range-Doppler, CFAR, etc.)
  • Experience with drone/UAS detection and classification
  • Familiarity with sensor fusion or multi-target tracking algorithms
  • Experience with edge deployment of ML models (embedded/low-power inference)
  • Experience with MLOps tooling and model versioning

Responsibilities

  • Develop and deploy machine learning models to classify objects detected by Fortem’s TrueView radar systems (e.g., drones, birds, aircraft, clutter)
  • Analyze large volumes of radar returns and track data to identify features that improve detection and classification accuracy
  • Collaborate with radar and FPGA engineering teams to understand signal processing pipelines and identify opportunities for data-driven improvements
  • Build data pipelines and labeling workflows to curate and maintain high-quality training datasets
  • Extend classification capabilities to additional sensor modalities and drone telemetry data over time
  • Evaluate model performance in production, monitor for drift, and iterate on models and features
  • Communicate findings, tradeoffs, and recommendations to engineering and product stakeholders
  • Other related duties and tasks as assigned by engineering leadership
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