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Principal Data Scientist, AI at Jobgether. This position is posted by Jobgether on behalf of Cambridge Mobile Telematics (CMT). We are currently looking for a Principal Data Scientist, AI in Massachusetts (USA).. As a Principal Data Scientist, AI, you’ll play a central role in designing and deploying cutting-edge AI systems built on real-world sensor and telematics data. You'll lead the charge in developing multi-modal and time-series models that improve risk prediction, crash detection, and driver behavior analysis. Working at the intersection of AI research and practical implementation, you'll collaborate across teams to turn innovation into impactful, scalable products. This is a high-impact role for someone with deep technical knowledge and a passion for solving complex problems in dynamic, real-world environments.. . Accountabilities. . Lead the end-to-end lifecycle of advanced AI models, including pre-training, fine-tuning, optimization, and deployment.. . Design physics-aware and self-supervised learning algorithms tailored to sensor-rich, spatio-temporal data.. . Build and maintain robust, scalable ML pipelines using distributed computing frameworks (e.g., PyTorch DDP, Horovod, Ray).. . Integrate AI models into production systems, ensuring reliability, efficiency, and scalability across cloud and edge environments.. . Drive cross-functional collaboration with engineering, product, and research teams to translate AI breakthroughs into real-world telematics solutions.. . Mentor junior data scientists and contribute to the overall AI/ML strategy and roadmap.. . Stay current with AI advancements, promoting the adoption of relevant technologies, tools, and ethical practices.. . Engage in tasks related to model explainability, bias mitigation, and AI lifecycle management.. . Handle other related duties as they arise.. . . PhD or Master’s in AI, Computer Science, Physics, Math, or a related field.. . 7+ years of experience in AI/ML, including 3+ years developing and deploying foundation models (e.g., BERT, GPT).. . Proven expertise in multi-modal and time-series transformers, self-supervised learning, and spatio-temporal modeling.. . Proficiency in Python and libraries such as Pandas, NumPy, and scikit-learn.. . Deep hands-on experience with PyTorch (preferred) or TensorFlow for model development and deployment.. . Strong background in distributed training methods and large-scale ML workflows.. . Familiarity with cloud platforms (AWS, Azure, GCP), Docker, Spark, and Airflow for MLOps.. . Excellent problem-solving and communication skills, with a product-oriented mindset.. . Preferred: knowledge of XAI, model guardrails, and ethical AI practices; publications in top AI/ML venues.. . Company Location: United States.