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Ai Quant Jobs (NOW HIRING)

MFS

Quant Research Associate

Boston, MA · Hybrid

$10K/mo

The Quantitative Research Associates work with analysts and portfolio managers through the ... Familiarity with AI or with financial data software such as Bloomberg or FactSet is a plus.

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$98K

$169.7K

$259.5K

How much do ai quant jobs pay per year?

As of Jun 21, 2026, the average yearly pay for ai quant in the United States is $169,729.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,500.00 and $199,000.00 per year, depending on experience, location, and employer.

What is an AI Quant?

An AI Quant, or Artificial Intelligence Quantitative Analyst, is a professional who combines expertise in quantitative finance and machine learning to develop advanced trading strategies, risk models, or analytics tools. AI Quants use algorithms, statistical models, and large datasets to identify patterns, forecast market trends, and make data-driven investment decisions. They often work in hedge funds, investment banks, or proprietary trading firms, collaborating with software engineers and other quants to implement and optimize AI-driven financial models.

Is 40 too old to become an quant?

Age is not a strict barrier to becoming an AI Quant, as the role values skills in programming, mathematics, and finance, which can be developed at any age. Many professionals transition into quantitative roles later in their careers by gaining relevant certifications, such as CFA or advanced degrees, and building experience with tools like Python, R, or MATLAB.

How does an AI Quant typically collaborate with data scientists, traders, and software engineers within a financial institution?

AI Quants often work closely with data scientists to develop and refine machine learning models using financial data, ensuring models are statistically robust and actionable. They collaborate with traders to translate complex quantitative signals into trading strategies that are practical and aligned with market objectives. Additionally, AI Quants partner with software engineers to implement and optimize these models for real-time deployment, ensuring that the underlying code is scalable, efficient, and reliable. This cross-functional environment requires strong communication skills and adaptability, as priorities can shift with market movements and technological advancements.

What are the key skills and qualifications needed to thrive as an AI Quant, and why are they important?

To thrive as an AI Quant, you need a strong background in quantitative analysis, mathematics, statistics, and machine learning, often supported by an advanced degree in a quantitative field. Proficiency in programming languages like Python or C++, experience with data analysis libraries (such as NumPy, pandas, and TensorFlow), and familiarity with financial modeling tools are typically required. Strong problem-solving skills, attention to detail, and effective communication set top performers apart in this role. These skills are crucial for developing robust AI-driven trading strategies and ensuring accurate, data-driven decision-making in the fast-paced financial sector.

What is the difference between Ai Quant vs Data Scientist?

AspectAi QuantData Scientist
Required CredentialsAdvanced degrees in quantitative fields, certifications in machine learning or AIDegrees in computer science, statistics, or related fields; certifications vary
Work EnvironmentFinancial firms, hedge funds, or trading firms focusing on quantitative analysisTech companies, research labs, or any industry leveraging data analysis
Employer & Industry UsagePrimarily finance and trading industriesBroad across tech, healthcare, retail, and more
Common Search & Comparison IntentUnderstanding specialized quantitative roles in financeExploring data analysis careers across industries

Ai Quants focus on developing algorithms and models for financial markets, often requiring advanced quantitative skills and finance-specific knowledge. Data Scientists have a broader scope, applying statistical and machine learning techniques across various industries. While both roles involve data analysis and programming, Ai Quants are specialized in finance, whereas Data Scientists work in diverse sectors.

Do quants make 7 figures?

Quantitative analysts, or quants, working in finance or hedge funds can sometimes earn seven-figure salaries, especially at senior levels or in high-paying firms. However, such compensation is typically reserved for experienced professionals with specialized skills in mathematics, programming, and finance, and is not the norm for all quants.

Which 3 jobs will survive AI?

AI Quant roles in finance are likely to persist because they require specialized quantitative skills, understanding of financial markets, and the ability to interpret complex data. Jobs that involve creative thinking, emotional intelligence, and complex problem-solving, such as healthcare professionals, educators, and skilled trades, are also expected to remain resilient to automation. These roles often require human judgment and adaptability that AI cannot fully replicate.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior AI researcher, machine learning director, or AI executive, often requiring advanced skills in data science, programming, and domain expertise. These roles usually involve leadership, strategic planning, and significant experience, and they may be found in large tech companies or finance firms offering competitive compensation packages.
More about Ai Quant jobs
What cities are hiring for Ai Quant jobs? Cities with the most Ai Quant job openings:
What states have the most Ai Quant jobs? States with the most job openings for Ai Quant jobs include:
What job categories do people searching Ai Quant jobs look for? The top searched job categories for Ai Quant jobs are:
Ai Quant jobs near you
Infographic showing various Ai Quant job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $169,729 per year, or $81.6 per hour.

Talent Sourcer - Machine Learning & Quantitative Research

IMC

Chicago, IL • On-site

Other

Posted 13 days ago

Job description

As an ML and Quantitative Research Sourcer, you will be focused on identifying and engaging experienced talent across Machine Learning and Quantitative Research, mapping competitive talent landscapes, building relationships with high-caliber technical professionals, and developing creative sourcing strategies that strengthen IMC's presence within the broader ML and quantitative communities. You will partner closely with recruiters and hiring managers to deliver strong pipelines of experienced candidates, assess talent against our technical and cultural standards, and provide market insights that inform hiring strategy.

Your Core Responsibilities

  • Partner with recruiters and hiring managers to understand hiring needs across Machine Learning and Quantitative Research functions
  • Develop and execute creative sourcing strategies to identify and engage experienced talent
  • Build and maintain strong pipelines of passive candidates across key technical markets
  • Conduct market mapping and talent landscape analysis to support hiring strategy and workforce planning
  • Leverage LinkedIn Recruiter, GitHub, Kaggle, research publications/conferences, referrals, and other creative sourcing channels to identify and engage top technical talent.
  • Engage candidates with compelling outreach and ensure a positive candidate experience throughout the sourcing process
  • Track sourcing activity, pipeline health, and market insights to inform recruiting decisions
  • Collaborate closely with recruiting teams to continuously improve sourcing processes and effectiveness

Skills & Experience

  • Bachelor's Degree; 4-7 years of sourcing experience focused on technical hiring, ideally within Machine Learning, Quantitative Research, or related technical domains
  • Strong understanding of ML, AI, quantitative research, and broader technical talent landscapes
  • Demonstrated success building relationships with and engaging experienced technical talent in competitive markets
  • Experience identifying and engaging talent through channels such as GitHub, Kaggle, Hugging Face, LinkedIn Recruiter, research publications, and other technical communities
  • Proven ability to develop and execute sourcing strategies using data, feedback, and market insights
  • Collaborative approach with experience partnering closely with recruiters and hiring managers in fast-paced environments
  • Strong communication, organizational, and stakeholder management skills, with the ability to manage multiple searches simultaneously and operate with a high degree of ownership

Please note that immigration sponsorship is not offered for this specific opening.

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