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Scientific Machine Learning Jobs in Connecticut (NOW HIRING)

Partner directly with quantitative researchers to understand the needs of a specific machine learning project and collaboratively produce data that best fits the model and project * Transform raw ...

Partner directly with quantitative researchers to understand the needs of a specific machine learning project and collaboratively produce data that best fits the model and project * Transform raw ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

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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 Connecticut?

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

What cities in Connecticut are hiring for Scientific Machine Learning jobs?

Cities in Connecticut with the most Scientific Machine Learning job openings:

Infographic showing various Scientific Machine Learning job openings in Connecticut as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 21% Part Time, 2% Temporary, and 3% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Data Scientist, Machine Learning

Greenwich, CT • On-site

AQR
501 - 1,000 employees

$180K - $200K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 13 days ago


Key responsibilities

  • Partner with researchers to understand project data needs and produce suitable datasets and features.

  • Transform raw data into research-ready, project-specific datasets and perform feature generation.

  • Build data quality assurance, monitoring, and profiling workflows to ensure datasets are clean, traceable, and reliable.


Job description

About AQR Capital Management

AQR is a global investment management firm built at the intersection of financial theory and practical application. We strive to deliver superior, long-term results for our clients by seeking to filter out market noise to identify and isolate what matters most, and by developing ideas that stand up to rigorous testing. Underpinning this philosophy is an unrelenting commitment to excellence in technology - powering our insights and analysis. This unique combination has made us leaders in alternative and traditional strategies since 1998.

AQR takes a systematic, research-driven approach, applying quantitative tools to process fundamental information and manage risk. Our clients include institutional investors, such as pension funds, insurance companies, endowments, foundations and sovereign wealth funds, as well as financial advisors.

Your Role:

You will serve as a bridge between data engineering and quantitative research. Working directly with researchers, you will also be responsible for ensuring research datasets are accurate, traceable, and ready for machine learning by developing robust data preparation and quality workflows. Your responsibility is to deliver clean, reliable, project-specific datasets and features to the researcher. 

What You'll Do:

  • Partner directly with quantitative researchers to understand the needs of a specific machine learning project and collaboratively produce data that best fits the model and project
  • Transform raw structured and unstructured data into project-specific, research-ready datasets
  • Perform feature generation and deliver prepared datasets and features to the researcher for modeling and productionization
  • Resolve tagging, entity-matching, and linkage issues across signals, textual data, and securities
  • Build data quality assurance, quality monitoring, and profiling workflows through programmatic checks, LLM reviews where appropriate, targeted manual inspection, and feedback-driven iterative refinement
  • Build point-in-time mappings and knowledge graphs for mergers and acquisitions, bankruptcies, IPOs, and other corporate events
  • Examine and onboard alternative datasets
  • Ensure datasets are clean, traceable, and reliable for trading strategies
  • Work across different researchers and potentially concurrent projects as priorities and the scope of the role evolve
  • Communicate clearly with researchers and engineering partners

What You'll Bring:

  • 4+ years of relevant work experience
  • Strong Python programming skills, including hands-on experience with pandas and NumPy
  • Strong SQL skills and practical experience with PostgreSQL
  • Experience working with both structured data and unstructured or textual data
  • Experience with data quality, validation, monitoring, and profiling
  • Experience with Git, PyTest, CI/CD, and API development
  • Ability to reason carefully through edge cases, protect data integrity, and maintain clear documentation of data definitions, transformations, and quality checks
  • Ability to work independently, communicate clearly with technical and non-technical stakeholders, and manage work across multiple concurrent initiatives
  • Strong visualization skills

Preferred Qualifications:

  • Experience with scikit-learn, statistics, or advanced modeling techniques
  • Experience with entity resolution, entity matching, or knowledge graphs
  • Experience designing prompts and using LLM APIs for batched or large-scale investigation, validation, feature generation, and iterative refinement
  • Experience building LLM-based featurization workflows, including iterative refinement, validation, and automated testing
  • Experience with Claude Code, Codex, or AWS Bedrock
  • Experience with AWS, including S3 and Batch
  • Experience with distributed computing and large-scale data processing
  • Exposure to data governance, data cataloging, or related best practices
  • Strong Math and statistics skills
  • Experience with Pytorch
  • Prior experience in financial services, trading, quantitative research, or another research-driven environment

 Who You Are:

  • Rigorous, thorough, and highly attentive to detail
  • Collaborative and able to communicate effectively with researchers and engineering partners
  • Comfortable working in an evolving role and taking on a broad range of responsibilities

AQR is an Equal Opportunity Employer. EEO/VET/DISABILITY

The salary range for this role is expected to be $180,000 to $200,000.  This is the range that we in good faith believe is accurate for this role at the time of this posting.  We may ultimately pay more or less than the posted range, depending upon factors such as skills, experience, location, or other business and organizational needs.  This wage range may also be modified in the future.

This job is also eligible for an annual discretionary bonus.

We offer comprehensive package of benefits including paid time off, medical/dental/vision insurance, 401(k), and any other benefits to eligible employees.

Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company's sole discretion, consistent with the law.