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Scientific Machine Learning Jobs in Rhode Island

ResultStack

Sr. Data Scientist

Carolina, RI · On-site

Must be a US Citizen or Hold a Green Card ABOUT THE ROLE We are looking to hire a Data Scientist ... They will be well versed in AI & Machine Learning. Having Hands-On experience with LLM's, NLP ...

micro1 AI

AI Trainer - Microbiology Expert

Providence, RI · Remote

$70 - $90/hr

Prior experience developing or applying rubrics in scientific or educational contexts. * Experience with AI, machine learning, or annotation projects related to biology or microbiology. * Advanced ...

Varsity Tutors

Python Tutor

Providence, RI · Remote

$18 - $40/hr

Emphasizes readable, maintainable code and connects Python to machine learning, web scraping, scientific computing, and DevOps applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

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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 Rhode Island?

For Scientific Machine Learning jobs in Rhode Island, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Rhode Island look for?

The top searched job categories for Scientific Machine Learning jobs in Rhode Island are:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Rhode Island as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

Sr. Data Scientist

ResultStack

Carolina, RI • On-site

Full-time

Re-posted 25 days ago

Job description

Must be a US Citizen or Hold a Green Card


ABOUT THE ROLE

We are looking to hire a Data Scientist who can transform the

complexity of global shipping and logistics into clear, actionable

intelligence. You'll work across operations, engineering, and leadership

to build predictive systems that optimize routes, forecast demand, and

surface insights that keep cargo moving. This is a high-impact role at the

intersection of data science, operational expertise, and emerging AI.

CORE REQUIREMENTS

This candidate will have Dashboard & BI Tooling. Being Fluent in

Tableau, Power BI, Looker, or equivalent. They will be able to model data

and present it for both technical and executive audiences.

They will have Complex Data Fluency. Being very comfortable

wrangling large, noisy datasets --- EDI records, tracking logs, port data,

weather overlays, and multi-model feeds.

They will do Predictive Modeling. Having proven experience building

ML models from messy. High-dimensional datasets (time series, sensor

data, ETA prediction, etc.).

They will be well versed in AI & Machine Learning. Having Hands-On

experience with LLM's, NLP, computer vision, or operations research

applied to real-world logistics problems.

This candidate must also be excellent in Collaboration &

Communication. They can translate model outputs into business

decisions. They will also possess strong documentation habits and

cross-functional alignment skills.

WHAT YOU'LL WORK ON

Route optimization and transit time prediction models Anomaly

detection in shipment and carrier data Real-time operational

dashboards for fleet and port performance AI-assisted demand

forecasting for freight capacity planning Cross-team data

infrastructure and model deployment support.

REQUIREMENTS

6+ Years in shipping, freight, logistics, or supply chain,

Understand how cargo and data both move.

NICE TO HAVES

Python, R, or SQL --- scripting and querying at production scale.

Software Development Principles: version control (Git), CI/CD, API

integration. Familiarity with containerization (Docker) or cloud

platform (AWS, GCP, Azure). Experience building data pipelines or ETL

workflows.