Data Scientist

We are seeking a highly skilled Data Scientist to build, train, and deploy large-scale self-supervised "foundation" models for time-series, sensor, multimodal, and industrial scientific data. This role focuses on developing advanced deep learning architectures capable of learning rich representations from high-dimensional sequential signals, later fine-tuned for tasks such as:

• Anomaly/event detection
• Predictive maintenance
• Forecasting
• Classification
• Multi-sensor fusion
• Industrial/scientific modeling

This is a high-impact, research-driven role working with large datasets, complex sensor modalities, and distributed training infrastructure., 1. Foundation Model Development

• Build and train self-supervised and semi-supervised foundation models for time-series and multimodal data
• Fine-tune large models for domain-specific tasks
• Apply contrastive learning, masked modeling, temporal predictive coding, multimodal alignment, etc.
• Develop transfer learning, adapter, and prompt-based strategies for rapid downstream adaptation

2. Data & Signal Processing

• Process, augment, and engineer features for univariate/multivariate time-series datasets
• Analyze IoT sensor streams, industrial vibration/temperature data, audio, imagery, etc.
• Perform sampling, synchronization, denoising, artifact removal, and sensor quality checks
• Integrate time series with images, structured data, audio, and text

3. Advanced Machine Learning & Architectures

• Build models using:
• RNNs / GRU / LSTMs
• TCNs
• 1D/2D/3D CNNs
• Transformers (BERT, ViT, TimeSFormer)
• Graph Neural Networks
• Diffusion / generative architectures
• Multi-modal encoders and fusion models
• Evaluate model performance using:
• MSE, RMSE, R²
• F1, AUC, Precision/Recall
• DTW, correlation, similarity metrics
• IoU and event-based segmentation metrics

4. Software Engineering & Infrastructure

• Build production-ready pipelines for ingesting, cleaning, segmenting, and aligning large-scale multi-sensor datasets
• Develop in:
• Python (NumPy, Pandas, SciPy)
• PyTorch (Lightning, Distributed)
• TensorFlow/Keras
• JAX/Flax
• C++/CUDA for custom kernels
• Train models on:
• Multi-GPU and multi-node clusters
• Mixed-precision systems
• Distributed optimization (ZeRO, DDP, etc.)

5. Mathematical & Algorithmic Foundations

• Apply strong background in:
• Linear algebra, probability, and statistics
• Signal processing (Fourier, wavelets, Kalman filters, noise modeling)
• Optimization (stochastic, convex, non-convex)
• Numerical methods, ODE/PDE modeling, regularization techniques

6. Collaboration & Communication

• Partner with scientists, engineers, domain experts, and product teams
• Present model behavior insights, attention maps, and uncertainty quantification
• Communicate findings clearly to both technical and non-technical audiences

• MS or PhD in Computer Science, Data Science, AI, Engineering, or related fields
• 3+ years of experience in Data Science, Machine Learning, or AI
• Strong experience building and training deep learning models
• Experience working with time-series or sensor data
• Proficiency in Python, deep learning frameworks, and ML engineering best practices, * Experience with multimodal learning
• Experience with large-scale distributed training
• Background in industrial, scientific, or sensor-driven AI