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How much do internship biostatistics phd internship jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for internship biostatistics phd internship in the United States is $19.31, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $20.91 per hour, depending on experience, location, and employer.

What is an internship biostatistics PhD internship?

An Internship Biostatistics PhD Internship is a temporary position designed for PhD students in biostatistics or related fields to gain practical experience working on real-world data analysis projects, often within the pharmaceutical, biotech, or academic sectors. Interns apply statistical methods to clinical or biomedical data, collaborate with research teams, and develop skills essential for future careers in biostatistics. These internships typically last a few months and provide valuable exposure to industry practices, software, and research environments.

What types of projects do PhD-level biostatistics interns typically work on during their internship?

PhD biostatistics interns often contribute to real-world research projects, such as analyzing clinical trial data, developing or validating statistical models, and supporting the design of experimental studies. Interns usually collaborate with multidisciplinary teams that include statisticians, data scientists, and medical researchers, providing statistical expertise and insights. These projects may culminate in presentations or reports and often offer opportunities to publish or co-author scientific papers, enhancing both practical experience and academic credentials.

What are the key skills and qualifications needed to thrive as a biostatistics PhD intern?

To thrive as a Biostatistics PhD Intern, you need a solid foundation in statistical theory, data analysis, and a strong background in mathematics or statistics, typically supported by ongoing PhD studies. Familiarity with statistical programming languages such as R, SAS, or Python, and experience with data management systems are commonly required. Strong problem-solving skills, attention to detail, and the ability to communicate complex statistical concepts clearly make candidates stand out. These competencies are critical for accurately analyzing biomedical data, supporting research projects, and effectively collaborating with interdisciplinary teams.
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Infographic showing various Internship Biostatistics Phd Internship job openings in the United States as of August 2026, with employment types broken down into 10% Internship, 56% Full Time, 32% Part Time, 1% Temporary, and 1% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $40,174 per year, or $19.3 per hour.

Machine Learning Internship - PhD: 2027

SIG Susquehanna

Bala Cynwyd, PA • On-site

$80 - $120/hr

Other

Posted 5 days ago

Job description

Overview

Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data, algorithms, and markets.

As a Machine Learning Intern at Susquehanna, you’ll work on high-impact projects that closely reflect the challenges and workflows of our full-time research team. You’ll apply your technical expertise in machine learning and data science to real-world financial problems, while developing a deep understanding of how machine learning integrates into Susquehanna’s research and trading systems. You will leverage vast and diverse datasets and apply cutting‑edge machine learning at scale to drive data‑informed decisions in predictive modeling to strategic execution.

What You Can Expect
  • Conduct research and develop ML models to identify patterns in noisy, non‑stationary data
  • Work side‑by‑side with our Machine Learning team on real, impactful problems in quantitative trading and finance, bridging the gap between cutting‑edge ML research and practical implementation
  • Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches
  • Design and run experiments using the latest ML tools and frameworks
  • One‑on‑one mentorship from experienced researchers and technologists
  • Participate in a comprehensive education program with deep dives into Susquehanna’s ML, quant, and trading practices
  • Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior
  • Explore various aspects of machine learning in quantitative finance from alpha generation and signal processing to model deployment and risk‑aware decision making
What we’re looking for
  • Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field
  • Proven experience applying machine learning techniques in a professional or academic setting
  • Strong publication record in top‑tier conferences such as NeurIPS, ICML, or ICLR
  • Hands‑on experience with machine learning frameworks, including PyTorch and TensorFlow
  • Deep interest in solving complex problems and a drive to innovate in a fast‑paced, competitive environment
Why Join Us?
  • Work with a world‑class team of researchers and technologists
  • Access to unparalleled financial data and computing resources
  • Opportunity to make a direct impact on trading performance
  • Collaborative, intellectually stimulating environment with global reach
About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting‑edge technology, we excel in solving complex problems and pushing boundaries together.

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