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Data Scientist Machine Learning

Santa Monica, CA · On-site

$75K - $100K/yr

As a Data Scientist Machine Learning, you will work within a small data science team focusing on predictive modeling, natural language processing, computer vision, recommender systems, and OCR ...

TwinThread

Machine Learning Engineer

Charlottesville, VA · On-site

$90 - $120/hr

Understanding of relational databases * 3+ years of experience as a data scientist, machine learning engineer, or similar role * Solid understanding of the fundamentals of statistical modeling

LAZ Learning A-Z

Machine Learning Intern

Work with our psychometricians and data scientists to define a research problem, design an ... Write code in Python using machine learning frameworks and libraries to ingest and clean data ...

Stefanini

Machine Learning Engineer

Allen Park, MI · On-site

... science methodologies including Machine Learning (ML), predictive modeling, math, statistics, advanced analytics, etc. Key ResponsibilitiesUnderstand business requirements and analyze datasets to ...

Merican

Data Science & Machine Learning Engineer

$117K - $140K/yr

Senior Data Science & Machine Learning Engineer Location: Remote, USA (Client Location ZIP: 01730) Duration: 6 Months Contract to Hire We are seeking an experienced Senior Data Science & Machine ...

Calendly

Machine Learning Engineer

You will report to the head of Data Science & Machine Learning and will be responsible for building and operating ML-powered features that create magical experiences for our customers. Our team:

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Scientific Machine Learning information

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How much do scientific machine learning jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for scientific machine learning in the United States is $31.48, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $40.14 per hour, depending on experience, location, and employer.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

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What states have the most Scientific Machine Learning jobs?

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Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $65,473 per year, or $31.5 per hour.

Machine Learning Internship - PhD: 2027

SIG Susquehanna

Bala Cynwyd, PA • On-site

$80 - $120/hr

Other

Posted 4 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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