Data Scientist [207886]

Aquent; Houston (TX); On-site; USD 100,000 - 140,000; Full time

Benefits offered by this job

Relocation assistance

Hybrid work schedule

Professional development opportunities

Job summary

A leading technology firm is looking for a skilled professional in Houston, TX for a hybrid role focused on foundation model development. You'll engage in shaping AI technologies and work collaboratively with a team that drives innovations from complex, real-world data across various sectors. The ideal candidate will have a MS or Ph.D. in a relevant field and at least 3 years of experience in data science and AI, complemented by strong skills in Python and deep learning frameworks.

Qualifications

• MS or Ph.D. in Computer Science, Data Science, or related field.
• Minimum of 3 years of relevant experience in data science and AI.
• Proficiency in Python and libraries like NumPy, SciPy, Pandas.
• Experience with deep learning frameworks like PyTorch and TensorFlow.
• Ability to build and deploy large-scale self-supervised models.

Responsibilities

• Architect, train, and deploy foundation models from diverse data.
• Fine-tune models for tasks such as anomaly detection and forecasting.
• Implement data processing techniques for sensor data streams.
• Design deep learning architectures for heterogeneous data types.
• Drive innovation using self-supervised and semi-supervised learning.

Skills

Python expertise

Deep learning frameworks (PyTorch, TensorFlow)

Data engineering

Model interpretation skills

Collaboration and communication

Education

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

Tools

NumPy

SciPy

Pandas

PyTorch

TensorFlow

CUDA

Job description

Hybrid Onsite in Houston, TX: 2 days a week onsite, 3 days WFH

Relocation Assistance: Offered

Must be authorized to work in the US, will not sponsor

This is an exciting opportunity to join a pioneering team at the forefront of industrial and scientific innovation. As a key contributor, you will be instrumental in developing and deploying cutting-edge AI models that unlock profound insights from complex, real-world data. Your work will directly impact critical applications, driving advancements in areas like predictive maintenance, anomaly detection, and multi-modal sensor fusion, ultimately shaping the future of intelligent systems. Partnering with Aquent, you will contribute to a leading organization that is committed to pushing the boundaries of technology and making a tangible difference in various sectors.

About the Opportunity

Our partner is a global leader dedicated to solving some of the world’s most complex challenges through technological innovation. They are building the next generation of intelligent systems, leveraging vast amounts of sensor, time series, and multi-modal data to create predictive and analytical solutions. This role offers a unique chance to work on large-scale, self-supervised “foundation” models, transforming raw data into actionable intelligence across diverse industrial and scientific applications. You will be part of a collaborative environment where your expertise in machine learning and data science will drive significant advancements and create lasting impact.

What You Will Do

• Foundation Model Development: Architect, train, and deploy large-scale, self-supervised foundation models capable of learning rich representations from time series, sequential sensor data, textual, and vision data.
• Model Fine-Tuning & Application: Adapt and fine-tune models for tasks such as anomaly/event detection, predictive maintenance, forecasting, classification, and multi-modal sensor fusion for industrial and scientific applications.
• Advanced Data Processing: Implement processing, augmentation, and feature engineering techniques for diverse financial, industrial, IoT, medical, and other sensor streams, handling univariate and multivariate time series.
• Sensor Data Expertise: Apply deep expertise in analyzing diverse sensor modalities (e.g., accelerometers, temperature, vibration, audio, images), managing sampling rates, synchronization, and real-world noise/artifact handling.
• Multi-Modality Learning: Design and implement robust deep learning architectures that integrate heterogeneous data types (time series, images, text, audio, structured data) through cross-modal representation learning.
• Cutting-Edge ML Techniques: Drive innovation using self-supervised and semi-supervised learning methods, including masked modeling, contrastive methods, temporal predictive coding, and multimodal alignment and fusion.
• Model Architecture Exploration: Work with model architectures such as sequence models (RNNs, GRU/LSTM, TCN), 1D/2D/3D CNNs, Transformers, graph neural networks, diffusion/generative models, and multi-modal/fusion encoders.
• Scalable Solutions: Implement transfer learning and fine-tuning strategies at scale, including prompt/adapter-based methods, temporal domain adaptation, and few-shot learning for specialized tasks.
• Performance Evaluation: Define and apply evaluation metrics for regression/classification (MSE, F1, AUC), time series similarity (DTW, correlation), event detection/segmentation (IoU, accuracy), and alignment with business/end-user KPIs.
• High-Performance Software Development: Develop custom kernels and high-performance preprocessing solutions using Python (NumPy, SciPy, Pandas) and C++/CUDA.
• Deep Learning Infrastructure: Leverage and optimize frameworks such as PyTorch (Lightning, Distributed), TensorFlow/Keras, and JAX/Flax for large-scale training on multi-GPU, multi-node clusters with mixed-precision and ZeRO optimization.
• Robust Data Engineering: Build and maintain robust data pipelines for ingesting, cleaning, segmenting, and aligning large-scale, time-synchronized multi-sensor datasets.
• Mathematical Foundations: Apply strong knowledge in Linear Algebra, Probability & Statistics, Optimization, Signal Processing, and Numerical Methods.
• Collaborative Impact: Engage in cross-disciplinary teamwork with domain experts, engineers, product owners, and end-users, effectively communicating complex model behaviors, uncertainty quantification, and value impact.

Must-Have Qualifications

• MS or Ph.D. in Computer Science, Data Science and AI, or related quantitative field.
• Minimum of 3 years of relevant professional experience in data science and AI or related fields.
• Expert proficiency in Python, including libraries such as NumPy, SciPy, and Pandas.
• Experience with leading deep learning frameworks like PyTorch (including Lightning and Distributed training), TensorFlow/Keras, or JAX/Flax.
• Proven ability to build, train, and deploy large-scale, self-supervised foundation models.
• Solid understanding and practical experience with self-supervised and semi-supervised learning techniques.
• Experience with various model architectures, including sequence models (RNNs, GRU/LSTM, TCN), CNNs, and Transformers.
• Proficiency in developing robust data pipelines for large-scale, time-synchronized multi-sensor datasets.
• Strong foundational knowledge in Linear Algebra, Probability & Statistics, and Optimization.
• Practical experience with Signal Processing techniques.
• Excellent collaboration and communication skills, with the ability to present complex technical concepts clearly to diverse audiences.

Nice-to-Have Qualifications

• Experience with C++/CUDA for developing custom kernels and optimizing high-performance preprocessing.
• Familiarity with Transformer models such as BERT, ViT, or TimeSFormer.
• Knowledge of filters like Kalman or Savitzky–Golay in signal processing.
• Prior experience in industrial, IoT, medical, or financial sensor data domains.
• Experience with model interpretability, attention analysis, and uncertainty quantification.

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