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Machine Learning Quant Jobs in New York (NOW HIRING)

Man Group

Principal Quant

New York, NY

Experience with Machine Learning techniques is a plus * PhD or exceptional Masters / Bachelors ... quantitative subject * Expertise in Python and Linux environments * Comfortable proficiency in C ...

RunSybil

Machine Learning Engineer

New York, NY · On-site

MS or PhD in a quantitative discipline is nice but certainly not required - strong bias for ... Understanding of both modern and classic machine learning techniques * Equally comfortable with ...

Zencastr

Senior Machine Learning Engineer (Remote)

New York, NY · On-site +1

$114K - $157K/yr

A postgraduate degree in Machine Learning, Mathematics, Computer Science, or a related quantitative field * 5 + years experience in Python * 3+ years experience in machine learning research, with a ...

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Machine Learning Quant information

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$57.4K

$130.4K

$215K

How much do machine learning quant jobs pay per year?

As of Aug 18, 2026, the average yearly pay for machine learning quant in New York is $130,371.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,900.00 and $166,800.00 per year, depending on experience, location, and employer.

What is a machine learning quant?

A Machine Learning Quant is a specialist in quantitative finance who applies machine learning techniques to develop trading strategies, manage risk, and analyze financial data. They leverage statistical models, deep learning, and reinforcement learning to identify patterns in market data and optimize predictions. This role typically involves programming in Python or C++, working with large datasets, and collaborating with traders and researchers. Machine Learning Quants are employed by hedge funds, investment banks, and proprietary trading firms to gain a competitive edge in financial markets.

What are typical daily responsibilities for a machine learning quant in a financial firm?

As a Machine Learning Quant, your day often involves researching and developing predictive models using large financial datasets, backtesting quantitative strategies, and optimizing algorithms for speed and accuracy. You'll collaborate closely with traders, data engineers, and other quants to implement models in live trading environments and refine them based on performance feedback. Regular activities also include monitoring new data sources, adjusting to changes in the market, and documenting your methodologies for regulatory or team review. This multidisciplinary work environment offers the opportunity to continuously learn and directly impact trading outcomes.

What are the key skills and qualifications needed to thrive as a machine learning quant?

To thrive as a Machine Learning Quant, you need strong skills in quantitative analysis, programming (often in Python or C++), statistical modeling, and a solid foundation in applied mathematics, typically supported by a degree in a quantitative field such as mathematics, physics, computer science, or engineering. Familiarity with machine learning frameworks (like TensorFlow or PyTorch), financial data platforms, and certifications such as CFA or advanced degrees can be advantageous. Critical thinking, collaboration, and clear communication are key soft skills that enhance effectiveness in working with both technical and non-technical stakeholders. These competencies are crucial for building and validating models that inform high-stakes financial strategies and deliver value in fast-paced trading environments.

What are the most commonly searched types of Machine Learning Quant jobs in New York?

The most popular types of Machine Learning Quant jobs in New York are:

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

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

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

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

Machine Learning Quant jobs near you
Infographic showing various Machine Learning Quant job openings in New York as of August 2026, with employment types broken down into 100% Full Time. Highlights an 79% In-person, 10% Hybrid, and 11% Remote job distribution, with an average salary of $130,371 per year, or $62.7 per hour.

Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International, LLC

Stamford, CT • On-site

Full-time

Posted 28 days ago

Job description

Application Deadline: September 1, 11:59pm EST
Program Summary - Commercial Technology Internships
Company Overview:
Castleton Commodities International is a leading global energy commodities merchant and infrastructure asset investor. As a trader, CCI deploys capital on a proprietary basis in the physical and financial commodity markets, providing the Company with market insights and access. As a strategic investor and developer, CCI leverages its market expertise, operations capabilities, and industry knowledge to invest in, and develop, select commodity infrastructure assets. Our strategically integrated platform has generated strong risk-adjusted returns for our investors since our formation.
Position Overview:
CCI is developing a leading-edge Data Science platform, as staying at the forefront of data management and analytics is essential to our investment strategy. We are looking for motivated and detail-oriented Machine Learning Interns with a strong interest in quantitative analysis, particularly time series forecasting to join our Global Data Science team in Stamford, CT, Houston, TX, or New York City offices. Our Machine Learning Internship provides a unique opportunity to work with fundamental market data, generating insights that support our commercial trading business. You will be responsible for analyzing time series data related to market fundamentals in the Power, Natural Gas, and Oil sectors, helping to identify key supply and demand drivers. These insights will play a vital role in forecasting price movements and supporting risk management decisions.
Responsibilities:
  • Apply mathematical and statistical knowledge to enhance existing machine learning applications and explore new solutions.
  • Work closely with Data Scientists, Analysts, and Traders to design, implement, and optimize machine learning models for time series forecasting, including ARIMA/SARIMA, gradient boosting methods (e.g., XGBoost), LSTM networks, and linear regression-based approaches.
  • Assist in designing and implementing end-to-end data ingestion processes, ensuring seamless data flow to investing teams.
  • Work with desk heads, traders, and analysts to understand current data architecture, investment processes, and functional requirements for data science analysis.
  • Contribute to identifying and back-testing new data sets, leveraging machine learning techniques to drive insights.
  • Conduct ad hoc research on emerging project topics, including energy fundamental data, analytics trends, and best practices in big data and artificial intelligence.

Qualifications:
  • Currently pursuing a Bachelor's Degree or higher in Mathematics, Statistics, Physics, Computer Science or related technical field with a focus in Machine Learning.
  • Expected graduation date of Winter 2027 or Spring/Summer 2028.
  • Experience applying machine learning techniques such as regression, time series forecasting, deep learning, reinforcement learning, or predictive modeling to solve problems involving complex data patterns and market dynamics.
  • Strong programming experience in Python (preferred libraries: Pandas, NumPy, etc.)
  • Ability to communicate and interact with a wide range of users, from very technical to non-technical backgrounds.
  • Strong analytical skills with demonstrated attention to detail.

Apply