Data Scientist

CYNET Systems

United States

United States

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Über

Machine Learning And Foundation Model Development: Develop self-supervised and semi-supervised learning models for time series and multimodal data. Apply masked modeling, contrastive learning, temporal predictive coding, and multimodal alignment techniques. Design and implement advanced architectures including RNNs, LSTM/GRU, TCNs, CNNs (1D/2D/3D), Transformers (BERT, ViT, TimeSFormer), graph neural networks, and generative/diffusion models. Implement transfer learning, fine-tuning, domain adaptation, and few-shot learning approaches. Define and evaluate appropriate metrics including MSE, F1, AUC, DTW, IoU, and business KPIs. Software And Infrastructure: Develop high-performance code in Python (NumPy, SciPy, Pandas) and C++/CUDA for optimized data processing. Build and train models using PyTorch (Lightning, Distributed), TensorFlow/Keras, or JAX/Flax. Execute large-scale distributed training on multi-GPU and multi-node clusters. Implement scalable data loaders for long sequence processing. Design and maintain robust data engineering pipelines for large-scale multi-sensor datasets. Mathematical And Algorithmic Foundations: Apply strong foundations in linear algebra, probability, statistics, and optimization. Utilize signal processing techniques including Fourier/wavelet transforms, Kalman filtering, Savitzky–Golay filtering, and noise modeling. Apply numerical methods such as ODE/PDE solvers, inverse problem techniques, and regularization methods. Implement time-frequency analysis methods for complex systems. Collaboration And Communication: Collaborate with cross-functional teams including engineers, domain experts, product owners, and end-users. Present complex model behavior clearly, including interpretability, attention analysis, and uncertainty quantification. Communicate measurable business value and performance impact. Requirement/Must Have: MS or Ph.D. in Computer Science, Data Science, AI, or related field. 3+ years of relevant experience in data science and AI. Strong expertise in time series and signal processing. Hands-on experience with deep learning frameworks (PyTorch, TensorFlow, or JAX). Experience with distributed training and large-scale data processing. Strong mathematical foundations in statistics, optimization, and numerical methods. Experience building production-grade ML pipelines. Should Have: Experience with multimodal foundation models. Experience in industrial, medical, IoT, or scientific data environments. Experience with C++/CUDA for performance optimization. Experience implementing domain adaptation and few-shot learning strategies. Strong experience with data engineering and scalable infrastructure. Qualification And Education: Master’s or Ph.D. degree in Computer Science, Data Science, AI, or related field required. 3+ years of relevant professional experience required.

United States

Sprachkenntnisse

English

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