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Data Science Phd Jobs in Spring, TX (NOW HIRING)

Master's or PhD candidate wrapping up within the next year - we're building a pipeline toward full-time offers * Pursuing a degree in Data Science, Statistics, Computer Science, or a related ...

Master's/PhD in Computer Science, Data Science, or equivalent experience * 4-7 years of industry experience working with real-world datasets * Experience with Agile Scrum development methodology * C# ...

Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, Engineering, Mathematics, or a related field. * 3+ years of experience ...

PhD related to AI or a Master's degree in computer science or a related field and 5+ years of experience with GenAI and related technologies including machine learning. data analytics, and ...

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Data Science Phd information

What is a data science PhD?

A Data Science PhD is a doctoral-level degree focused on advanced research in data science, which combines elements of statistics, computer science, and domain expertise. Students in a Data Science PhD program typically work on developing new methods for analyzing large datasets, creating machine learning algorithms, and addressing complex problems in areas such as artificial intelligence, data mining, and predictive analytics. Graduates are prepared for careers in academia, research, and industry, where they can lead data-driven projects and contribute to advancements in the field.

What are the key skills and qualifications needed to thrive as a data science PhD?

To thrive as a Data Science PhD, you need advanced expertise in statistics, machine learning, data analysis, and a doctoral degree in a quantitative field. Proficiency in programming languages like Python or R, experience with big data frameworks (e.g., Spark, Hadoop), and familiarity with data visualization tools are typically required. Critical thinking, problem-solving, and strong communication skills help you translate complex data insights for diverse stakeholders. These skills are vital for driving innovative research, making data-driven decisions, and contributing impactful solutions in data-centric environments.

What are some common challenges faced by data science PhDs when transitioning from academia to industry roles?

Data Science PhDs often encounter challenges such as adapting to the faster pace and collaborative nature of industry projects compared to academic research. In industry, there is a greater emphasis on delivering practical solutions within tight deadlines and working closely with cross-functional teams like engineering and product management. Additionally, data science work in industry may require balancing technical rigor with business impact, often prioritizing actionable insights over exhaustive analysis. Building strong communication and stakeholder management skills can help ease this transition.

What can I do with a data science PhD?

A data science PhD prepares individuals for advanced roles in research, analytics, and machine learning across industries such as technology, finance, healthcare, and academia. Graduates can work as data scientists, machine learning engineers, research scientists, or data analysts, often utilizing programming languages like Python or R and tools such as TensorFlow or SQL. The degree also enables roles involving complex data modeling, statistical analysis, and developing innovative data-driven solutions.

What job categories do people searching Data Science Phd jobs in Spring, TX look for?

The top searched job categories for Data Science Phd jobs in Spring, TX are:

What cities near Spring, TX are hiring for Data Science Phd jobs?

Cities near Spring, TX with the most Data Science Phd job openings:

Infographic showing various Data Science Phd job openings in Spring, TX as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

Data Science Internship

Houston, TX

Fervo Energy
Clean Energy Semiconductors Manufacturing • 11 - 50 employees

Full-time, Internship

Posted 9 days ago


Job description

Description

Internship Overview 


You won't be running coffee orders or shuffling paperwork this summer. At Fervo, interns are handed something real: a project of your own, scoped with your manager on day one and yours to drive for the full 12 weeks. You'll work side-by-side with the teams building the next generation of geothermal energy, tackling problems that genuinely move the business forward. At the end of the summer, you'll present your work to our executive leadership team, department leads, and fellow interns, sharing real results with a real audience. This is a real seat at the table - and a real shot at what comes next. 


Position Description


Fervo Energy is developing next-generation geothermal power to deliver firm, carbon-free energy at scale, anchored by our flagship Cape Station development in Milford, Utah. As a Data Scientist Intern, you'll join Fervo's Data Science team to help build models and analyses that turn data into decisions across the business. 

You'll work alongside engineers and data scientists to explore datasets, build predictive models, and translate findings into insights that inform real decisions across drilling, operations, and commercial teams. 

Requirements

 Responsibilities 

  • Explore and analyze datasets to identify trends and opportunities 
  • Build and validate predictive models and statistical analyses 
  • Support development of machine learning models for real-world business problems 
  • Communicate findings and recommendations to technical and non-technical stakeholders 

Required Qualifications 

  • Master's or PhD candidate wrapping up within the next year - we're building a pipeline toward full-time offers 
  • Pursuing a degree in Data Science, Statistics, Computer Science, or a related quantitative field 
  • Strong written and verbal communication skills, including comfort presenting to stakeholders and leadership 
  • Eagerness to learn, take initiative, and adapt quickly to new challenges 
  • Proficiency in Python and common data science libraries (e.g., Pandas, NumPy, Scikit-learn) 

Preferred Qualifications 

  • Experience with machine learning frameworks (e.g., PyTorch, TensorFlow) 
  • Familiarity with SQL and data visualization tools 
  • Interest in applying data science to energy or industrial problems