Data Scientist

• The Data Scientist will design and develop advanced machine learning and deep learning solutions focused on time series, multi-sensor, and multimodal data.
• This role requires strong expertise in signal processing, foundation models, scalable training, and cross-domain collaboration.
• The ideal candidate will work on complex real-world datasets across industrial, financial, IoT, medical, or scientific domains, delivering high-performance models that drive measurable business impact.

Data And Signal Processing:

• Process and engineer features for univariate and multivariate time series data.
• Analyze diverse sensor modalities including accelerometers, vibration, temperature, audio, images, and other sensor streams.
• Handle synchronization, sampling rate variations, and real-world noise/artifact mitigation.
• Develop multi-modality learning pipelines integrating time series, images, text, audio, and structured data.
• Implement cross-modal representation learning and fusion architectures.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Master s or Ph.D. degree in Computer Science, Data Science, AI, or related field required.
• 3+ years of relevant professional experience required.