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SLB · 13 hours ago

Data Scientist

Houston, TX

Full-time

Onsite

Mid Level

3+ years exp

SLB is a company focused on leveraging advanced data science techniques for industrial and scientific applications. They are seeking a Data Scientist to build, train, and deploy large-scale models that analyze time series and sensor data for tasks like anomaly detection and predictive maintenance.

Computer Software

Growth Opportunities

H1B Sponsor Likely

Responsibilities

Build, train, and deploy large-scale, self-supervised 'foundation' models that learn rich representations of time series, sequential sensor data in addition to textual and vision data, to be fine-tuned for tasks such as anomaly/event detection, predictive maintenance, forecasting, classification, or multi-modal sensor fusion for industrial and scientific applications

Time Series & Sequential Data: processing, augmentation, feature engineering for financial, industrial, IoT, medical, or other sensor streams (univariate/multivariate time series)

Sensor Data Analysis: expertise with diverse sensor modalities (e.g., accelerometers, temperature, vibration, audio, images), sampling rates, synchronization, and real-world noise/artifact handling

Multi-Modality Learning: integrating heterogeneous data types (time series, images, text, audio, structured) into robust deep learning architectures; cross-modal representation learning

Self-supervised and Semi-supervised Learning: time series foundation models, masked modeling, contrastive methods, temporal predictive coding, multimodal alignment and fusion

Model Architectures: sequence models (RNNs, GRU/LSTM, TCN), 1D/2D/3D CNNs, Transformers (BERT, ViT, TimeSFormer), graph neural networks, diffusion/generative models, multi-modal/fusion encoders

Transfer Learning & Fine-Tuning at Scale: prompt/adapter-based strategies, temporal domain adaptation, few-shot learning for specialized tasks

Evaluation Metrics: regression/classification (MSE, F1, AUC), time series similarity (DTW, correlation), event detection/segmentation (IoU, accuracy), business/end-user KPIs

Programming: expert Python (NumPy, SciPy, Pandas), C++/CUDA for custom kernels and high-performance preprocessing

Deep Learning Frameworks: PyTorch (Lightning, Distributed), TensorFlow/Keras, JAX/Flax

Large-scale Training: multi-GPU, multi-node clusters, mixed-precision, ZeRO optimization, scalable data loaders for long sequences

Data Engineering: robust pipelines for ingesting, cleaning, segmenting, and aligning large-scale, time-synchronized multi-sensor datasets

Linear Algebra, Probability & Statistics, Optimization (stochastic, convex/non-convex, Bayesian)

Signal Processing: Fourier/wavelet analysis, filters (Kalman, Savitzky–Golay), resampling, noise modeling

Numerical Methods: ODE/PDE solvers, inverse problems, regularization, time-frequency methods for complex systems

Cross-disciplinary teamwork with domain experts, engineers, product owners, and end-users from industrial, scientific, or medical backgrounds

Clear presentation of complex model behaviors (interpretability, attention analysis), uncertainty quantification, and value impact

Qualification

Time Series ProcessingSelf-supervised LearningDeep Learning FrameworksPython ProgrammingMulti-Modality LearningModel ArchitecturesData EngineeringMathematical FoundationsPh.D. in AICollaboration SkillsCommunication Skills

Required