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Physics Informed Machine Learning Jobs in Cypress, TX

... management of machine learning and advanced analytics solutions across upstream Oil & Gas ... Knowledge of time-series, forecasting, or physics-informed ML workloads. * Experience with ...

Postdoctoral Fellow - Imaging Physics

Houston, TX · On-site

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... Experience with machine learning and deep learning techniques, statistical modeling, biomechanical ...

Postdoctoral Fellow - Imaging Physics

Houston, TX · On-site

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... Experience with machine learning and deep learning techniques, statistical modeling, biomechanical ...

Postdoctoral Fellow - Imaging Physics

Houston, TX · On-site

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... Experience with machine learning and deep learning techniques, statistical modeling, biomechanical ...

Nuclear Physicist

Sugar Land, TX · On-site

$90 - $115/hr

SLB is seeking applicants for an engineering scientist position in the domain of nuclear physics ... Experience with machine learning and data science is an advantage but is not required.

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Physics Informed Machine Learning information

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How much do physics informed machine learning jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for physics informed machine learning in Cypress, TX is $17.28, according to ZipRecruiter salary data. Most workers in this role earn between $10.77 and $21.97 per hour, depending on experience, location, and employer.

What is a physics informed machine learning?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the typical challenges faced by professionals working in physics informed machine learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What are the key skills and qualifications needed to thrive in the physics informed machine learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are popular job titles related to Physics Informed Machine Learning jobs in Cypress, TX?

For Physics Informed Machine Learning jobs in Cypress, TX, the most frequently searched job titles are:

What job categories do people searching Physics Informed Machine Learning jobs in Cypress, TX look for?

The top searched job categories for Physics Informed Machine Learning jobs in Cypress, TX are:

What cities near Cypress, TX are hiring for Physics Informed Machine Learning jobs?

Cities near Cypress, TX with the most Physics Informed Machine Learning job openings:

Infographic showing various Physics Informed Machine Learning job openings in Cypress, TX as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $35,945 per year, or $17.3 per hour.

Senior Machine Learning Data Scientist

Socket.dev

Houston, TX • On-site

$102 - $156.40/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 26 days ago


Job description

Senior Machine Learning Data Scientist

Location: Houston, United States, 77056

Company: ENGIE North America Inc.

Business Unit: Supply & Energy Management

Division: BP B2B US

Employment Type: Permanent, Full-Time

What You Can Expect

As our Senior Machine Learning Data Scientist, you will support the development, delivery, and continuous improvement of forecasting processes and models for ENGIE's U.S. power supply business. Working under the guidance of the Portfolio & Load Analytics Manager, you will collaborate closely with portfolio managers, risk, IT, and other stakeholders to ensure forecasting outputs are accurate, timely, and aligned with operational needs.

In this role, you will leverage rigorous data collection, validation, and analysis to improve forecast performance and support portfolio management, hedging strategies, and risk management activities across U.S. power markets.

Position is based in Houston, TX, and reports to the Portfolio & Load Analytics Manager.

  • You will be actively involved in the design, implementation, and continuous improvement of forecasting tools, models, and methodologies, while helping build a modern forecasting platform for the US power market. Your role will include validating forecast inputs and outputs, monitoring model performance from a data science standpoint, and ensuring model drift and forecast quality remain under control over time. You will also work cross-functionally within the broader forecasting community and with forecasting teams in other countries to promote knowledge sharing, consistency, and the cross-pollination of ideas and best practices.
  • You will be responsible not only for the technical development of forecasting solutions, but also for ensuring their operational reliability and relevance to business needs. This includes building models across the full lifecycle—from feature engineering, training, tuning, and execution to validation, monitoring, and ongoing refinement—while maintaining strong software engineering standards that deliver dependable, production‑grade forecasts. Because these forecasts are used to support commercial decisions across ENGIE's U.S. power business, reliability, traceability, and robustness are essential parts of the role.
  • In addition to model development, you will provide scientific expertise for bespoke analyses and contribute to the continuous improvement of forecasting practices, performance measurement, and platform capabilities. A strong understanding of forecasting methodology, model governance, and production‑quality software development is essential to succeed in this role, along with the ability to translate complex analytical outputs into practical business value for stakeholders across the organization.
What You'll Bring
  • You hold a Bachelor's degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Engineering, Finance, or Economics, or a related field. In lieu of a degree, we will consider a combination of relevant experience that demonstrates strong quantitative rigor and practical business acumen.
  • You have the technical expertise to build, analyze, and productionize forecasting models, along with the communication skills needed to align diverse stakeholders around a clear and informed point of view.
  • Minimum of five (5) years of experience building and deploying forecasting models and data pipelines using Python and SQL, with ownership of production deliverables.
  • You have a strong foundation in probability, statistics, data science, and machine learning, with practical experience in time series forecasting.
  • You have advanced proficiency in Python, SQL, Git, and modern data platforms such as Databricks and Spark, with the ability to build scalable data pipelines and automate workflows.
  • You are knowledgeable in developing and deploying forecasting models, including regression, time series, and machine learning techniques, with experience improving forecast accuracy and backcasting performance.
  • You have experience designing and maintaining end‑to‑end forecasting systems including data ingestion, feature engineering, model training, hyperparameter tuning, deployment, and performance monitoring.
  • You are knowledgeable in energy markets, load forecasting, or commodity trading, and understand how market dynamics and regulatory changes impact forecasting outputs.
  • You are an effective communicator who can translate complex analytical insights into clear recommendations for both technical and non‑technical stakeholders, including portfolio managers, traders, and risk teams.
  • You have strong analytical and problem‑solving skills, with the ability to manage multiple priorities, work independently, and deliver accurate, high‑quality results in a fast‑paced environment.
Additional Details
  • Role is eligible for our hybrid work policy.
  • Must be willing and able to comply with all ENGIE ethics and safety policies.
Compensation

Salary Range: $102,000 - $156,400 USD annually.

This represents the average expected pay range for a qualified candidate.

ENGIE complies with all federal, state, and local minimum wage laws. Actual salary offered may vary depending on geography, experience, education, internal pay alignment, or other bona fide factors.

In addition to base pay, this position is eligible for a competitive bonus / incentive plan.

Benefits

Our comprehensive benefits package includes options for medical, dental, vision, life insurance, employer‑paid short‑term and long‑term disability insurance, ESPP, generous paid time off including wellness days, holidays and leave programs. We also help you plan for retirement by offering a 401(k) Retirement Savings Plan with a company match. These benefits support your well‑being and that of your family at all stages of life.

Equal Opportunity

ENGIE North America is an equal opportunity employer and is firmly committed to creating an inclusive workplace for all employees. We are committed to providing employees with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status.

If you need assistance with this application or a reasonable accommodation due to a disability, you may contact us at ENGIENA-ENGIEHR@engie.com. This email address is reserved for individuals with disabilities in need of assistance and is not a means of inquiry regarding positions or application status.

We are unable to sponsor or take over sponsorship of an employment visa for this role at any time.

The safety of our employees is our number one priority. All employees at ENGIE have both a duty and the authority to STOP WORK if unsafe acts are observed.

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