Data Scientist

Annual Salary (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. Nesco Resource offers a comprehensive benefits package for our associates, which includes a MEC (Minimum Essential Coverage) plan that encompasses Medical, Vision, Dental, 401K, and EAP (Employee Assistance Program) services. Nesco Resource provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

Created: 2026-03-04