Sr Data Scientist

GC-Squared Resources, LLC; Houston (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

A leading engineering and data science firm is seeking a Senior Data Scientist to work on physics-based modeling and equipment reliability. The successful candidate will develop models that enhance maintenance strategies and collaborate with various teams. Key requirements include a Bachelor's degree and significant experience in data science and engineering methods. This role is based in Houston, TX and is an onsite position.

Qualifications

• 5+ years of experience applying physics-based modeling or reliability engineering in industrial settings.
• 3+ years building and deploying data-science algorithms on cloud platforms.

Responsibilities

• Define and guide the development of physics-ML reliability models.
• Perform fluid-mechanics and mechanical simulations in Python or MATLAB.
• Architect, deploy and monitor model training and serving pipelines.

Skills

Python programming for simulations

Weibull analysis

Survival modeling

FMEA

Strong communication skills

Education

Bachelor's degree in Mechanical Engineering, Petroleum Engineering, Physics or related field

Tools

Python

GCP AI Platform

Dataiku

Job description

Senior Data Scientist, Physics-Based Modeling & Equipment Reliability

Reports to: Data Science Manager

Location: Houston, TX (oully onsite)

Brief Description

The Completions Data Science team brings together talented, driven professionals who turn complex operational data into clear insights and empowers our client to make smarter, data-backed decisions every day. You will work specifically with Operations and Maintenance departments to develop physics-driven models and reliability frameworks that predict equipment behavior and optimize maintenance strategies for the frac fleet.

Detailed Description

In this role you will collaborate with Program Managers to lead the strategy and execution of hybrid physics and machine-learning models for equipment reliability. You will design and run first-principles simulations, integrate high-frequency telemetry into digital twins, deploy scalable model pipelines in the cloud and validate predictions against field data.

Key Responsibilities

• Define and guide the development of physics-ML reliability models (Weibull analysis, survival modeling, FMEA)
• Perform fluid-mechanics and mechanical simulations in Python or MATLAB and integrate outputs with telemetry streams
• Architect, deploy and monitor model training and serving pipelines on GCP AI Platform or Dataiku
• Establish validation protocols by coordinating with subject-matter experts to calibrate model assumptions
• Partner with maintenance, operations and field teams to align modeling efforts with business needs and data availability
• Identify new digital-twin use cases and build proof-of-concepts for early-warning systems and maintenance optimization
• Present technical findings to operations leadership, maintenance planners and engineering management

Job Requirements

• Prior experience in equipment reliability, predictive maintenance or physics-based modeling in oil and gas
• Expert programming skills in Python (SciPy, NumPy) for simulation and model development
• Strong foundation in reliability engineering methods such as Weibull analysis, survival modeling and FMEA
• Strong communication skills with the ability to explain complex models to non-technical stakeholdersAbility to manage multiple priorities and deliver results on time

Minimum Qualifications

• Bachelor's degree in Mechanical Engineering, Petroleum Engineering, Physics or related field
• 5+ years of experience applying physics-based modeling or reliability engineering in industrial settings
• 3+ years building and deploying data-science algorithms on cloud platforms (AWS, GCP or Azure)
• 3+ years developing simulation code in Python

Preferred Qualifications

• Master's degree or higher in a quantitative engineering or physical science discipline
• Research publications or patents in equipment reliability, preventative maintenance or related areas
• Prior field experience in equipment maintenance
• Experience integrating physics-based models with machine-learning frameworks such as TensorFlow or PyTorch