Lead Data Scientist with PyTorch

EPAM Systems; USA; Remote; USD 150.000 - 190.000; Vollzeit

Zusammenfassung

EPAM Systems is seeking a Lead Data Scientist specialized in PyTorch to collaborate with Geoscientists, Data Scientists, and ML Engineers on seismic data projects.

Responsibilities include building 2D/3D seismic models (U-Nets, CNNs), improving existing tools, validating outputs with well data, and exploring new business opportunities in upstream oil & gas using computer vision and DL.

Qualifikationen

• Minimum 5 years in ML/Data Science roles with hands-on DL on real-world image or volumetric data.
• Strong PyTorch and Python proficiency.
• Experience with 2D/3D seismic datasets; upstream oil & gas domain knowledge helpful.

Aufgaben

• Build and apply deep learning models (U-Nets, CNNs) to denoise and interpret seismic data.
• Improve accuracy and performance of seismic ML tools through retraining and hyperparameter tuning.
• Collaborate with Geoscientists to validate outputs against well data and expert interpretations.
• Identify new business opportunities where DL could enhance exploration workflows.
• Keep up with CV and seismic ML research for real-world applications.

Kenntnisse

PyTorch

Python

Seismic data

UNet

CNNs

Image segmentation

Deep learning

MLOps

Docker

Cloud services

Tools

MLflow

Docker

Azure

AWS

GCP

Jobbeschreibung

We're looking for a Lead Data Scientist skilled in PyTorch to collaborate with a multidisciplinary team of Geoscientists, Geophysicists, Data Scientists, and Machine Learning Engineers. The role involves translating technical and business requirements into reliable, high-impact deep learning solutions for seismic data, while also enhancing existing seismic ML tools and uncovering new business applications.

Responsibilities

• Build and apply deep learning models (U-Nets, CNNs, and related architectures) to denoise and structurally interpret 2D and 3D seismic data
• Enhance the accuracy, robustness, and performance of current seismic ML tools through retraining, architecture improvements, and hyperparameter optimization
• Create models for structural interpretation needs, including fault detection, horizon tracking, and seismic facies classification
• Collaborate with Geoscientists and Geophysicists to validate outputs against expert interpretations and well data, incorporating their input into model refinements
• Spot, assess, and recommend new business opportunities where computer vision and deep learning could benefit exploration and production workflows
• Keep pace with advances in computer vision and seismic ML research, evaluating new techniques for real-world application
• Maintain sound engineering standards for reproducible experiments, model versioning, and production deployment

Requirements

• A minimum of 5 years in comparable roles (ML Engineer, Data Scientist, or Geophysicist with strong ML expertise), with practical experience applying deep learning to real-world image or volumetric datasets
• Solid grasp of Computer Vision concepts, such as image segmentation, denoising, feature extraction, and convolutional architectures
• Strong command of PyTorch for constructing, training, and assessing deep learning models
• Deep knowledge of U-Net, CNN, and similar architectures applied to 2D and 3D seismic datasets
• Experience within the Upstream Oil & Gas sector, collaborating with Geoscience teams and seismic datasets
• Familiarity with seismic data fundamentals, including acquisition and processing principles, seismic attributes, noise characteristics, and structural elements like faults and horizons
• Excellent Python programming abilities
• Nice to have Exposure to transformer-based or self-supervised learning models for vision or seismic applications
• Knowledge of MLOps platforms (such as MLflow), Docker, and cloud-based AI services (Azure, AWS, or GCP)