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Data Science Physics Jobs in Texas (NOW HIRING)

Recru

Data Scientist

Houston, TX

Bachelor's in Computer Science, Data Science, Statistics, Engineering, Physics, Applied Mathematics, or related quantitative field. * 3+ years of experience in machine learning, applied AI, or ...

Michaels Stores

Manager - Data Science

Irving, TX · On-site

Statistics, Economics, Applied Math, Operations Research, Physics, Data Science fields) Applicants in the U.S. must satisfy federal, state, and local legal requirements of the job. At The Michaels ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · On-site

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

GM Financial

Sr Data Scientist

Fort Worth, TX · Hybrid

... Computer Science, Physics or similar quantitative field required (a combination of education ... The Senior Data Scientist is responsible for Implementing the design, development, deployment, and ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · Remote

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

Cymertek Corporation

Data Scientist

San Antonio, TX · On-site

... Science, Data Science, Statistics, Mathematics, Applied Mathematics, Engineering, Economics, Physics, Operations Research, Information Technology, etc. • TS/SCI Full Poly clearance • Full U.S.

Battelle

Data Scientist II

San Antonio, TX · On-site

D. with 0-2 years of experience in Physics, Physics engineering, Mathematics, Biology, Chemistry, Statistics, or Engineering with a minor, certificate, or emphasis in computer science, data science ...

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Data Science Physics information

See Texas salary details

$21.5K

$81.3K

$176.3K

How much do data science physics jobs pay per year?

As of Jul 16, 2026, the average yearly pay for data science physics in Texas is $81,340.00, according to ZipRecruiter salary data. Most workers in this role earn between $37,375.00 and $120,922.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Data Science Physics position, and why are they important?

To thrive in Data Science Physics, you need strong analytical abilities in both physics and statistics, typically supported by an advanced degree in physics, data science, or a related field. Familiarity with tools such as Python, MATLAB, machine learning libraries (e.g., scikit-learn, TensorFlow), and experience using simulation or data visualization software are essential. Excellent problem-solving, collaboration, and communication skills help you work effectively with multidisciplinary teams and explain complex findings to non-experts. These competencies enable efficient analysis and interpretation of large scientific datasets, driving innovation and informed decision-making in research and industry settings.

What does a typical day look like for someone working in Data Science Physics?

A typical day in Data Science Physics often involves collecting, cleaning, and analyzing large datasets derived from experimental or simulated physics research. You may spend time developing and testing predictive models, interpreting results, and visualizing data to communicate findings to colleagues and stakeholders. Collaboration is common, with regular meetings alongside scientists, engineers, and data professionals to discuss project goals or troubleshoot challenges. Additionally, you may contribute to research publications or help develop new methodologies for data analysis, making each day varied and intellectually stimulating.

What is a Data Science Physics job?

A Data Science Physics job combines physics principles with data science techniques to analyze complex datasets, build predictive models, and extract insights. Professionals in this role apply statistical methods, machine learning, and computational algorithms to solve problems in areas such as material science, astrophysics, and engineering. They often work with big data, simulations, and experimental data to improve decision-making and research outcomes.

What are the most commonly searched types of Data Science Physics jobs in Texas? The most popular types of Data Science Physics jobs in Texas are:
What are popular job titles related to Data Science Physics jobs in Texas? For Data Science Physics jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Data Science Physics jobs in Texas look for? The top searched job categories for Data Science Physics jobs in Texas are:
What cities in Texas are hiring for Data Science Physics jobs? Cities in Texas with the most Data Science Physics job openings:
Data Science Physics jobs near you
Infographic showing various Data Science Physics job openings in Texas as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, 2% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $81,340 per year, or $39.1 per hour.

Data Scientist

Recru

Houston, TX

Full-time

Posted 7 days ago

Job description

Role Overview

We are seeking a Data Scientist to help build the next generation of industrial intelligence for our operations, reliability, maintenance, and performance optimization. This role sits at the intersection of applied machine learning, large-scale industrial telemetry, physics-informed analytics, and cloud software platforms.

You will develop and productionize advanced AI/ML models that transform high-frequency operational turbine data into actionable customer intelligence — reducing forced outages, improving availability, lowering O&M costs, and enabling predictive operations across fleets of industrial assets.

Key Responsibilities

Machine Learning

  • Design, develop, and deploy machine learning models for:
  • Predictive maintenance, Anomaly detection, Failure prediction, Remaining useful life (RUL) estimation, Operational optimization, Fleet-wide analytics
  • Build and train models using large-scale industrial telemetry and operational datasets.
  • Apply advanced ML techniques including:
  • Time-series forecasting, Deep learning, Statistical modeling, Unsupervised learning, Physics-informed ML approaches
  • Develop algorithms capable of handling noisy, sparse, and real-world operational data.
  • Evaluate model performance using operational KPIs and real-world production feedback.

Production ML & MLOps

  • Build scalable production pipelines to operationalize ML models into customer-facing products.
  • Develop infrastructure for:
  • Feature engineering, Automated retraining, Model monitoring, Drift detection, Experiment tracking, CI/CD for ML workflows
  • Deploy models across cloud and edge-computing environments.
  • Collaborate closely with software engineering teams to integrate ML capabilities into SaaS applications and operational workflows.

Cross-Functional Collaboration

  • Partner with controls engineers, reliability engineers, product managers, and software teams to solve complex industrial problems.
  • Translate operational challenges into scalable data science solutions.
  • Communicate technical findings and recommendations to both technical and non-technical stakeholders.
  • Contribute to technical strategy and mentor junior engineers and data scientists.

Required Qualifications

  • Bachelor’s in Computer Science, Data Science, Statistics, Engineering, Physics, Applied Mathematics, or related quantitative field.
  • 3+ years of experience in machine learning, applied AI, or production data science systems.
  • Strong proficiency in:
  • Python, SQL, Scientific computing and data engineering workflows
  • Experience with modern ML frameworks and tools such as:
  • PyTorch, TensorFlow, Scikit-learn, XGBoost, Spark
  • Experience building and deploying production ML systems in cloud environments (AWS, Azure, or GCP).
  • Strong understanding of:
  • Time-series analytics, Statistical inference, Feature engineering, Distributed systems, Production software engineering practices
  • Experience with containerization and orchestration tools such as Docker and Kubernetes is a plus.

Preferred Qualifications

  • Experience in industrial systems, IIoT, energy, power generation, aerospace, or reliability engineering.
  • Familiarity with:
  • Data Streaming platforms (Azure/AWS/GCP services), MLflow, Real-time analytics systems
  • Experience deploying ML systems in operationally critical or high-availability environments.
  • Knowledge of digital twins, edge AI, or physics-informed machine learning techniques.

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