Data Scientist

Our client is looking for a Data Scientist to design, train, and deploy large-scale foundation models for time series, sensor, and multimodal data. This role focuses on self-supervised learning, sequence modeling, and production-grade ML systems that deliver measurable business impact.

• Design and train large-scale self-supervised and semi-supervised models for time series and sensor data
• Implement techniques such as masked modeling, contrastive learning, temporal predictive coding, and multimodal alignment
• Develop transfer learning and fine-tuning strategies (adapters, domain adaptation, few-shot)
• Engineer features for univariate and multivariate time series across industrial, IoT, financial, or medical domains
• Apply advanced signal processing (Fourier/wavelets, Kalman, Savitzky–Golay, time-frequency methods)
• Build and optimize sequence models (RNN, LSTM/GRU, TCN),
  CNNs
  , Transformers
  , GNNs
  , and generative/diffusion models
• Train models using PyTorch, Tensor Flow/Keras, or JAX with distributed/multi-GPU strategies
• Optimize performance with C++/CUDA when needed
• Define and track model metrics (MSE, F1, AUC, DTW, IoU, business KPIs)
• Partner cross-functionally and communicate model insights, interpretability, and uncertainty

• 3+ years of experience in machine learning / AI / applied data science
• Strong expertise in time series and multimodal ML
• Experience with distributed model training at scale
• Proven ability to transition research → production systems
• Strong foundation in statistics, optimization, and numerical methods
• Experience building foundation models in industrial, scientific, or medical domains
• Background in advanced signal processing or physics-informed ML
• Open-source contributions or peer-reviewed publications
• Experience with multi-sensor data (vibration, audio, vision, etc.)

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

Hire Power 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.