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Data Science Phd Research Jobs (NOW HIRING)

Canva

PhD Research Scientist Intern

San Francisco, CA · On-site

$157K/yr

You'll gain hands on experience with real data, production infrastructure and real deadlines, while ... As a Generative AI Research Scientist Intern at Canva, you'll advance the frontier of agentic AI ...

Simular

PhD Research Intern

Palo Alto, CA · On-site

What you'll do As a PhD intern, you will ... Collaborate with research scientists to advance methods in: * Planning and RL for computer use (e.g ...

University of Utah

Research Scientists (PhD)

Campus, IL · On-site

$99K - $135K/yr

Details Open Date 07/27/2026 Requisition Number PRN45749B Job Title Research Scientists (PhD ... Strong technical expertise in experimental design, data analysis, and scientific communication.

Mercer University

$20.40/hr

... data on the My Information page. IMPORTANT: Please review the job posting and fully complete all ... Biomedical Sciences Supervisor: Juli Langston Job Title: PHD Research Assistant-Invite Only This is ...

Fgcu

Postdoctoral Researcher

Campus, IL · On-site

Strong quantitative, programming, or scientific communication skills are desirable. Postdoctoral ... data analysis, and development of AI-enabled agricultural decision-support tools. PhD Research ...

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Data Science Phd Research information

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$122.7K

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How much do data science phd research jobs pay per year?

As of Sep 14, 2026, the average yearly pay for data science phd research in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a data science PhD research?

A Data Science PhD research position involves conducting in-depth, original research in the field of data science, often within a university or academic setting. Researchers in this role work on advanced topics like machine learning, statistical modeling, big data analytics, and artificial intelligence. The goal is to push the boundaries of knowledge in data science, publish academic papers, and contribute to solving complex problems using data-driven approaches. Such positions typically require strong analytical, programming, and mathematical skills, as well as the ability to communicate findings through publications and presentations.

What are some common challenges faced by data science PhD researchers when collaborating with interdisciplinary teams?

Data Science PhD researchers often work with teams from diverse fields such as engineering, medicine, or social sciences. A common challenge is bridging the gap between technical concepts and domain-specific knowledge, which requires strong communication skills and adaptability. Additionally, aligning research goals and expectations across disciplines can be complex, as each field may have different methodologies and success metrics. Overcoming these challenges helps foster innovative solutions and broadens the impact of your research.

What are the key skills and qualifications needed to thrive as a data science PhD researcher, and why are they important?

To thrive as a Data Science PhD Researcher, you need expertise in statistical analysis, machine learning, programming (often Python or R), and a solid academic background in computer science, mathematics, or a related field. Familiarity with data visualization tools, big data platforms (like Hadoop or Spark), and version control systems, as well as relevant publications or conference presentations, is typically expected. Strong analytical thinking, problem-solving abilities, and effective communication skills help researchers excel and collaborate within interdisciplinary teams. These skills enable researchers to drive innovative discoveries, communicate findings clearly, and contribute impactful solutions to complex data-driven problems.

What is the difference between Data Science Phd Research vs Data Analyst?

AspectData Science Phd ResearchData Analyst
Required CredentialsPhD in Data Science, Statistics, or related fieldBachelor's or Master's in related fields
Work EnvironmentAcademic, research institutions, or R&D departmentsBusiness, corporate, or consulting firms
Employer & Industry UsagePrimarily academia, research labs, or specialized R&D teamsBusiness analytics, marketing, finance, and operations

Data Science Phd Research involves advanced research, theoretical development, and academic publishing, often in academic or research settings. In contrast, Data Analysts focus on interpreting existing data to generate actionable insights for businesses. While both roles require strong analytical skills, the PhD research role emphasizes deep theoretical knowledge and experimentation, whereas Data Analysts prioritize practical data handling and reporting.

What are popular job titles related to Data Science Phd Research jobs?

For Data Science Phd Research jobs, the most frequently searched job titles are:

Infographic showing various Data Science Phd Research job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 12% Part Time, and 2% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Graduate/PhD Research Intern, Machine Learning

Seattle, WA • On-site

Constellation Space
See more jobs at Constellation Spaceexternal

$6.0K - $7.0K/mo

Full-time

Re-posted 4 days ago

Job description

The Role

Constellation is seeking an ambitious Graduate or PhD Research Intern to join our Data Science / AI team. You will research and develop cutting-edge machine learning models to solve complex aerospace challenges, from predictive maintenance to autonomous orbital navigation.

Responsibilities

  • Conduct independent research to design and train novel machine learning architectures.

  • Analyze massive datasets derived from flight telemetry and satellite sensors.

  • Prototyping and testing algorithms in simulated aerospace environments.

  • Publish internal papers and present findings to the core engineering team.

Requirements

  • Currently pursuing a Master's or Ph.D. in Computer Science, Aerospace Engineering, Mathematics, or a related field.

  • Strong theoretical understanding of deep learning, computer vision, or reinforcement learning.

  • Proficiency in Python and standard ML frameworks (PyTorch, TensorFlow, or JAX).

  • Ability to work on-site in Seattle for the duration of the internship.