Data Scientist

FieldBridge energy is looking for a Data Scientist to work for the world’s largest oilfield service company. Pay/Schedule: $K-K/year (stock options and other incentives included) | Hybrid: days in-office, days remote | Full-time permanent position | Relocation assistance available

Position Overview: Build, train, and deploy large-scale, self-supervised foundation models that learn rich representations of time series, sequential sensor data, textual, and vision data. Fine-tune models for anomaly/event detection, predictive maintenance, forecasting, classification, and multi-modal sensor fusion for industrial and scientific applications.

Key Responsibilities:

• Build and train large-scale foundation models using self-supervised and semi-supervised learning methods for time series, sensor, text, and vision data

• Process, augment, and engineer features for diverse sensor modalities including accelerometers, temperature, vibration, audio, and images with real-world noise handling

• Integrate heterogeneous data types (time series, images, text, audio, structured data) into robust deep learning architectures with cross-modal representation learning

• Implement transfer learning and fine-tuning strategies at scale using prompt/adapter-based methods, temporal domain adaptation, and few-shot learning

• Collaborate with cross-disciplinary teams including domain experts, engineers, and product owners to deliver interpretable models with quantified uncertainty and business impact

Required Qualifications:

• MS or PhD in Computer Science, Data Science, AI, or related field required

• + years relevant experience in data science and AI

• Expert Python programming (NumPy, SciPy, Pandas) and C++/CUDA for custom kernels

• Deep expertise in PyTorch (Lightning, Distributed), TensorFlow/Keras, or JAX/Flax

• Strong experience with self-supervised/semi-supervised learning: masked modeling, contrastive methods, temporal predictive coding, multimodal alignment

• Proficiency with sequence models (RNNs, GRU/LSTM, TCN), CNNs, Transformers (BERT, ViT, TimeSFormer), graph neural networks

• Experience with large-scale training: multi-GPU/multi-node clusters, mixed-precision, ZeRO optimization

• Strong foundation in time series analysis, signal processing (Fourier/wavelet analysis, filters), and sensor data processing

• Expertise in data engineering: building robust pipelines for large-scale, time-synchronized multi-sensor datasets

• Strong mathematical background: linear algebra, probability, statistics, optimization, numerical methods

• Excellent communication skills with ability to present complex model behaviors and value impact to technical and non-technical stakeholders

• Must be authorized to work in the US