Data Scientist

Data Scientist

JOB-10045934

Anticipated Start Date

March 9, 2026

Location

Houston, TX

Type of Employment

Direct Hire

Employer Info

Our client is a global leader in energy technology, providing cutting-edge solutions across the oil and gas industry. Operating in over 100 countries, they focus on digital innovation and sustainable practices to drive the future of energy and support the transition to lower-carbon operations.

Job Summary

We are seeking a highly skilled Data Scientist - Machine Learning Scientist to build, train, and deploy large-scale, self-supervised foundation models that learn rich representations from time series, sequential sensor data, and multi-modal inputs (text, vision, audio, structured data).

Job Description

• Design and train large-scale, self-supervised and semi-supervised foundation models for time series and sensor data.
• Implement masked modeling, contrastive learning, temporal predictive coding, and multimodal alignment techniques.
• Develop transfer learning and fine-tuning strategies (prompt/adapters, domain adaptation, few-shot learning).
• Process and engineer features for univariate and multivariate time series (industrial, financial, IoT, medical, etc.).
• Handle diverse sensor modalities (accelerometers, vibration, temperature, audio, images).
• Address synchronization, sampling rates, real-world noise, and artifact correction.
• Apply signal processing techniques including Fourier/wavelet analysis, filtering (Kalman, Savitzky–Golay), and time-frequency methods.
• Develop and optimize sequence models (RNNs, GRU/LSTM, TCNs).
• Build CNNs (1D/2D/3D), Transformers (BERT-style, ViT-style, Time-aware variants), graph neural networks, and generative/diffusion models.
• Design multi-modal fusion architectures for heterogeneous data integration.
• Define and evaluate metrics including: Regression/classification (MSE, F1, AUC), Time-series similarity (DTW, correlation), Event detection/segmentation (IoU, accuracy), Business and end-user KPIs
• Develop production-grade systems in Python (NumPy, SciPy, Pandas).
  
• Optimize performance with C++/CUDA when required.
  
• Train models at scale using PyTorch (Lightning, Distributed), TensorFlow/Keras, or JAX/Flax.
  
• Implement multi-GPU and multi-node distributed training (mixed precision, ZeRO optimization).
  
• Build scalable data pipelines for ingesting, cleaning, segmenting, and aligning large multi-sensor datasets.
  
• Apply advanced knowledge of linear algebra, probability, statistics, and optimization (stochastic, convex/non-convex, Bayesian).
  
• Utilize numerical methods (ODE/PDE solvers, inverse problems, regularization).
  
• Implement robust signal processing and noise modeling approaches for complex systems.
  
• Partner with cross-disciplinary teams including engineers, domain experts, product stakeholders, and end-users.
  
• Present complex model behavior clearly, including interpretability analysis and uncertainty quantification.
  
• Translate AI insights into measurable business and operational impact.

Skills Required

• 3+ years of relevant experience in machine learning, AI, or applied data science.
• Strong experience with time series and multi-modal machine learning.
• Experience training models at scale in distributed environments.
• Demonstrated ability to move research concepts into production systems.
• Preferred: Experience developing foundation models for industrial, scientific, or medical domains.
  
• Preferred: Background in advanced signal processing or physics-informed modeling.
  
• Preferred: Contributions to open-source ML projects or peer-reviewed publications.

Education

• MS or Ph.D. in Computer Science, Data Science, AI, or related field.

Salary

• $130K - $210K per year (stock options, and other incentives) (Compensation will be offered within this posted range based on experience, skills, and market factors)

HirePower Personnel, Inc. is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, disability, protected veteran status, or other characteristics protected by law.