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Manager Machine Learning Finance Jobs in Raleigh, NC

... machine learning techniques to solve various business problems in the banking and financial ... management in Banking and Financial Services. • Expertise in Predictive Modeling and Big Data ...

This role sets technical direction and ensures the team applies sound statistical and machine learning practices, while remaining grounded in real-world outcomes. The Manager is expected to stay ...

This role sets technical direction and ensures the team applies sound statistical and machine learning practices, while remaining grounded in real-world outcomes. The Manager is expected to stay ...

This role sets technical direction and ensures the team applies sound statistical and machine learning practices, while remaining grounded in real-world outcomes. The Manager is expected to stay ...

Data Scientist

Cary, NC · On-site

$65 - $70/hr

... or financial industry experience with sales, marketing, and/or customer engagement analytics ... resource management, and cloud-native architectures. Skills: * Machine Learning. * Artificial ...

... machine learning, or AI, with at least 8 years in leadership positions. • Demonstrated experience in managing and scaling data science teams of 15+ professionals. • Proven record of delivering ...

... machine learning, or AI, with at least 8 years in leadership positions. • Demonstrated experience in managing and scaling data science teams of 15+ professionals. • Proven record of delivering ...

Showing results 41-60

Manager Machine Learning Finance information

See Raleigh, NC salary details

$40.8K

$120.9K

$164.3K

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

As of Aug 20, 2026, the average yearly pay for manager machine learning finance in Raleigh, NC is $120,855.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,900.00 and $163,300.00 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.

AI ML Data Scientist

ClifyX

Cary, NC • On-site

Full-time

Re-posted 26 days ago


Job description

Job Summary:
ClifyX is a company specializing in data science and analytics, and they are seeking an AI ML Data Scientist. The role requires expertise in predictive modeling and big data analytics, with a focus on implementing machine learning techniques to solve various business problems in the banking and financial services sector.
Responsibilities:
• Data Scientist with 5-10+ years of result-oriented, hands-on professional experience with a successful record of accomplishments in Data Science & Analytics and Project management in Banking and Financial Services.
• Expertise in Predictive Modeling and Big Data Analytics, Statistical model development, Implementations & Optimization techniques
• Proficiency in implementing Machine learning techniques -Regression, Decision Tree Learning, Neural networks, Random Forest and XGBoost-in various business problems (AML, fraud detection, mortgage default, foreclosure, credit risk management, price prediction and optimization)
• Strong leadership and capacity to work as a team player, as well as excellent communication skills
• Some knowledge on various aspects of Retail and Wholesale Consumer banking and US Mortgage Banking.
Qualifications:
Required:
• Machine Learning techniques
• Unsupervised - K-means Clustering, PCA - Dimension Reduction, Kernel Density Estimations
• Supervised - Regression, Decision Trees, Random forest, XG Boost algorithm
• Time series - Exponential models, Holt-Winters, ETS, Hybrid
• ARIMA & GARCH
• Deep Learning - Neural Network, Recurrent Neural Networks
• Database - SQL, Advance SQL, Oracle, NoSQL
• Data Science Languages - SAS, SAS Enterprise Miner, R Programming, Python, Spark
• Statistical & Data Management Packages - Python - Pandas, Numpy, sklearn, PyOdbc
• R- dplyr, car, caret, lubridate, zoo, Rminer, R-Odbc
• Visualization - Tableau, Shiny, ggplot2, dygraphs, matplotlib, seaborn
• Big Data Technologies - Spark (Pyspark & SparkR), Hadoop, Yarn
• PM Tools - MS Project, MS Visio, TFS, JIRA
• Cloud, Web frameworks & Virtualization - Azure, Flask, Docker & Kubernetes, Kafka
• 5-10+ years of result-oriented, hands-on professional experience with a successful record of accomplishments in Data Science & Analytics and Project management in Banking and Financial Services
• Expertise in Predictive Modeling and Big Data Analytics, Statistical model development, Implementations & Optimization techniques
• Proficiency in implementing Machine learning techniques - Regression, Decision Tree Learning, Neural networks, Random Forest and XGBoost-in various business problems (AML, fraud detection, mortgage default, foreclosure, credit risk management, price prediction and optimization)
• Strong leadership and capacity to work as a team player, as well as excellent communication skills
• Some knowledge on various aspects of Retail and Wholesale Consumer banking and US Mortgage Banking
Company:
ClifyX provides innovative business solutions which satisfy requirements for mission-critical reliability, scalability, interoperations. Founded in 1998, the company is headquartered in South Plainfield, USA, with a team of 501-1000 employees. The company is currently Late Stage.

ClifyX logo

About ClifyX

Sourced by ZipRecruiter

ClifyX is a well-established player in the IT Services sector that specializes in providing result-oriented technological solutions to a wide range of industrial verticals. Based in South Plainfield, New Jersey, ClifyX offers a comprehensive selection of IT services that include project staffing, application development, professional consulting, and other IT-based solutions. While the company's website, clifyx.com, does not divulge the exact founding date, it is clear that ClifyX has grown into a renowned name within their domain, thanks to their unwavering commitment to innovative practices. The company's mission statement revolves around harnessing the power of technology to assist their clientele in steering their respective businesses towards success.

Industry

Recruiting and staffing services

Company size

51 - 200 Employees

Headquarters location

South Plainfield, NJ, US

Year founded

1998