ZipRecruiter

Log In

1

Scientific Machine Learning Jobs in New York (NOW HIRING)

Oxman

Machine Learning Research Engineer

New York, NY · On-site

$142K - $184K/yr

Working across disciplines-from architecture and ecology to materials science and computation, we ... Develop machine learning models for geospatial inference of key ecosystem metrics, leveraging ...

Spgi

Machine Learning Engineer

New York, NY · On-site +1

$209K - $250K/yr

Job Requirements: Master's degree in Computer Science, Statistics, Data Science, or related ... Machine Learning (ML) and artificial intelligence (Al) tools Data Preprocessing, Exploration and ...

previous page

Showing results 41-60

All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in New York

Scientific Machine Learning information

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.

What are popular job titles related to Scientific Machine Learning jobs in New York?

For Scientific Machine Learning jobs in New York, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in New York look for?

The top searched job categories for Scientific Machine Learning jobs in New York are:

What cities in New York are hiring for Scientific Machine Learning jobs?

Cities in New York with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Machine Learning Researcher - Intern

Point72

New York, NY • On-site

Internship

Re-posted 10 days ago

Job description


Researchers are responsible for applying, adapting, and extending existing results in the broad field of machine learning, while also conducting novel research as required. We are interested in all aspects of ML including: predictive modelling, clustering, time series analysis, natural language processing, and computer vision. Successful researchers manage all aspects of the research process including methodology selection, data collection and analysis, implementation and testing, prototyping, and performance evaluation.
Some successful researchers have joined us from similar backgrounds at other firms. Others have joined from related fields or directly from academia and have thrived with hands on guidance from our large team of experienced portfolio managers and researchers. Our most exceptional team members combine strong technical skills and a passion for problem solving with an intense curiosity about financial markets and human behavior.
DESIRABLE CANDIDATES
  • Students enrolled in a PhD program in machine learning, computer science, statistics, or a related field.
  • Superb analytical and quantitative skills, along with a healthy streak of creativity.
  • Demonstrated ability to conduct independent research utilizing large data sets.
  • Passion for seeing research through from initial conception to eventual application.
  • Curiosity about financial markets
  • Strong scientific programming in Python, R or Matlab.
  • Empirical, detail-oriented mindset.
  • Sense of ownership of his/her work, working well both independently and within a small collaborative team.

We're looking for exceptional colleagues with unparalleled passion. If you'd like your resume to stand out, tell us about your exceptional personal achievements, even if they have nothing to do with finance. Of course we love to hear more about specific engineering or data projects that you've worked outside of school, or as part of your curriculum. If you're proud of the work you did we want to hear about it. In addition to exceptional statisticians and engineers, we work with talented musicians, writers, mathematicians, and founders of non-profits; we'd love to learn more about what excites you.

Apply