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Manager Machine Learning Finance Jobs in Wheaton, IL

IMC

Machine Learning Researcher

Chicago, IL · On-site

$250K - $300K/yr

Manage data acquisition, preprocessing, and feature engineering for structured and unstructured ... machine learning, and engineering shape how modern markets are traded. A stabilizing force in ...

Crowe LLP

Machine Learning Intern

Chicago, IL · On-site

... change management controls for risk and compliance policies Presenting work clearly to technical and non-technical audiences Relevant majors and areas of expertise Data Science Computer Science ...

Crowe LLP

Machine Learning Intern

Oakbrook Terrace, IL

$27 - $42/hr

... change management controls for risk and compliance policies Presenting work clearly to technical and non-technical audiences Relevant majors and areas of expertise Data Science Computer Science ...

Crowe LLP

Machine Learning Intern

Chicago, IL · On-site

$27 - $42/hr

... change management controls for risk and compliance policies Presenting work clearly to technical and non-technical audiences Relevant majors and areas of expertise Data Science Computer Science ...

Coinflow

Machine Learning Lead

Chicago, IL · On-site

$225K - $275K/yr

... global financial infrastructure with stablecoins, AI-driven fraud prevention, and instant ... Experience developing, managing, and scaling MLOps pipelines and monitoring systems (retraining ...

Capital One

Lead Machine Learning Engineer

Chicago, IL · On-site

$105K - $139K/yr

Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk ... Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other ...

Capital One

Lead Machine Learning Engineer

Chicago, IL · On-site +1

$105K - $139K/yr

Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk ... Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other ...

Capital One

Lead Machine Learning Engineer

Chicago, IL · On-site

$105K - $139K/yr

Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk ... Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other ...

Capital One

Lead Machine Learning Engineer

Chicago, IL · On-site +1

$105K - $139K/yr

Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk ... Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other ...

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Showing results 21-40

All JobsMachine Learning Finance JobsManager Machine Learning Finance JobsManager Machine Learning Finance Jobs in Wheaton, IL

Manager Machine Learning Finance information

See Wheaton, IL salary details

$40.6K

$120.2K

$163.3K

How much do manager machine learning finance jobs pay per year?

As of Sep 3, 2026, the average yearly pay for manager machine learning finance in Wheaton, IL is $120,162.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,300.00 and $162,400.00 per year, depending on experience, location, and employer.

What does a manager of machine learning in finance do?

A Manager of Machine Learning in Finance oversees teams that develop and implement machine learning models to solve financial problems, such as risk assessment, fraud detection, and algorithmic trading. They coordinate with data scientists, engineers, and business stakeholders to ensure models meet regulatory standards and align with company goals. Additionally, they are responsible for project management, mentoring team members, and staying updated with advancements in both finance and artificial intelligence.

How does a manager of machine learning in finance typically collaborate with cross-functional teams?

A Manager of Machine Learning in Finance often works closely with data scientists, software engineers, financial analysts, and business stakeholders. They are responsible for translating business problems into machine learning solutions and ensuring models meet both technical and regulatory requirements. Regular meetings and clear communication are essential, as the manager must align team efforts with organizational goals, facilitate knowledge sharing, and integrate model outputs into financial decision-making processes. Collaboration also involves coordinating with IT for data infrastructure and with compliance teams to uphold data privacy standards.

What are the key skills and qualifications needed to thrive as a manager of machine learning in finance, and why are they important?

To thrive as a Manager of Machine Learning in Finance, you need strong expertise in machine learning, statistics, and financial analysis, typically supported by a relevant advanced degree and experience in both data science and finance. Familiarity with programming languages like Python or R, cloud platforms, and machine learning frameworks such as TensorFlow or Scikit-learn is essential, along with knowledge of regulatory compliance systems. Exceptional leadership, strategic thinking, and communication skills set top candidates apart by enabling effective team management and cross-functional collaboration. These skills and qualities are crucial to drive innovative solutions, ensure regulatory adherence, and deliver business value in a complex financial environment.

What is the difference between Manager Machine Learning Finance vs Data Scientist Finance?

AspectManager Machine Learning FinanceData Scientist Finance
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or Finance; certifications in machine learning or data analysisBachelor's or Master's in Data Science, Statistics, or related fields; often includes certifications in data analysis or programming
Work EnvironmentLeads teams, manages projects, collaborates with stakeholders in financeAnalyzes data, develops models, supports decision-making in finance teams
Employer & Industry UsageFinancial institutions, hedge funds, investment firmsFinancial firms, banks, fintech companies

The Manager Machine Learning Finance oversees teams and projects applying machine learning to finance problems, focusing on leadership and strategy. In contrast, Data Scientists in finance primarily analyze data and develop models to support financial decisions. Both roles require strong technical skills, but the manager role emphasizes team management and project oversight.

Can manager machine learning finance be used in finance?

A Manager of Machine Learning in Finance oversees the development and implementation of machine learning models to improve financial analysis, risk management, and trading strategies. This role involves skills in data science, programming, and finance, and is used to enhance decision-making processes and automate tasks within financial institutions.

What cities near Wheaton, IL are hiring for Manager Machine Learning Finance jobs?

Cities near Wheaton, IL with the most Manager Machine Learning Finance job openings:

Manager Machine Learning Finance jobs near you
Infographic showing various Manager Machine Learning Finance job openings in Wheaton, IL as of July 2026, with employment types broken down into 84% Full Time, 14% Part Time, and 2% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $120,162 per year, or $57.8 per hour.

Machine Learning Researcher

IMC

Chicago, IL • On-site

$250K - $300K/yr

Full-time

PTO

Re-posted 7 days ago

Job description

IMC Trading is seeking quantitative researchers with a proven track record to apply state-of-the-art machine learning & deep learning to solve challenging trading problems. This role is part of a central ML research team that collaborates across trading teams at IMC. The ideal candidate will have experience working with other researchers and engineers to build and continuously improve models, systems, and research tooling. We firmly believe that success for research-driven efforts lies in bringing together skills in ML, statistics, and trading intuition as well as a problem-solving mindset and pragmatism. This is an opportunity to dive deep into feature engineering and alpha research, and focus on applying a wide range of ML models as well as to perform research on building custom models.
Your Core Responsibilities:
  • Design and deploy machine learning models to enhance trading performance across various asset classes
  • Research, test and prototype new algorithmic ideas; deploy advanced ML techniques applicable to market prediction, signal generation, and portfolio optimization
  • Collaborate with quantitative traders, researchers, and developers to translate market insights into data-driven features and models
  • Manage data acquisition, preprocessing, and feature engineering for structured and unstructured data sources

Your Skills and Experience:
  • PhD or Master's in Engineering, Math, Statistics, Computer Science, or related quantitative field
  • 2+ years of experience building applied ML models; previous experience in trading environment preferred
  • Proven expertise in developing and deploying predictive models
  • Strong programming skills in Python; proficiency in ML libraries such as PyTorch, TensorFlow, and/or high-performance libraries like Jax
  • Strong understanding of theoretical foundations of state-of-the-art ML models
  • Strong publication track record at ICML, ICLR, NeurIPS, or equivalent
  • Ability and desire to work in a collaborative team environment
  • Excellent written and verbal communication skills

The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.
Salary Range
$250,000-$300,000 USD
About Us
IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.

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