Data Scientist

Hybrid - 2 days in Office 3 days WFH

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 (Num Py, Sci Py, Pandas), C++/CUDA for custom kernels and high-performance preprocessing.
• Deep Learning Frameworks:
  PyTorch (Lightning, Distributed), Tensor Flow/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.

• MS / Ph.D. in computer science, data science and AI or related fields.
• 3+ years of relevant experience in data science and AI or related fields.

US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit

US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.