Data Scientist Direct Hire

Description: This is a Direct Hire Role NAK - NAK (stock options, and other incentives) Hybrid - 2 days in Office 3 days WFH Interviews will be 1st 15 min phone screen 2nd Technical Interview 1hr 3rd 30min with Hiring Manager 4th Onsite 4Hours Onsite Relocation = yes Must be authorized to work in the US 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. Data/Signal Processing • 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. Machine Learning & Foundation Model Expertise • 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. Software & Infrastructure • 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. Mathematical & Algorithmic Foundations • 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. Collaboration & Communication • 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. Enable Skills-Based Hiring No Worker Training Location Additional Details Worker Training Location : Location Is this role bonus eligible? : Yes Is this a Casual Worker? : No Please enter in Shift, Shift Premiums, Hours and Rotation Specifics for this role : 40 Select Type of Work Location : Office Please select Environment Type : OnShore Will the position be required to wear respirator? : No Please Select Driver Type : Non-Driver

Created: 2026-03-04