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Fraud Detection Machine Learning Jobs in Philadelphia, PA

JP Morgan Chase

CCB Risk Program Associate

Wilmington, DE · On-site

$57K - $57K/yr

... visual anomaly detection, synthetic data/image generation, and GenAI-enabled workflows-across ... Design and develop machine learning models to drive impactful fraud modeling, covering the entire ...

ASCENSUS

Treasury Analyst

Philadelphia, PA · On-site

... fraud detection and related treasury activity. * Record journal entries related to cash ... An "AI-first" mindset with hands-on experience utilizing generative AI and machine learning tools ...

ASCENSUS

Treasury Analyst

Dresher, PA · On-site

... fraud detection and related treasury activity. * Record journal entries related to cash ... An "AI-first" mindset with hands-on experience utilizing generative AI and machine learning tools ...

SEI Investments

AI Cybersecurity Engineer

Oaks, PA · On-site

Engineer AI-powered security detection systems leveraging machine learning for threat hunting, anomaly detection, and behavioral analytics. Cross-Functional Collaboration & Stakeholder Management

Deloitte

Cyber - SecOps - Consultant

Philadelphia, PA · On-site

Developing and applying analytics, machine learning, and data science techniques to cyber use cases across security operations, threat detection, and incident response * Working with technologies ...

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Fraud Detection Machine Learning information

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How much do fraud detection machine learning jobs pay per hour?

As of Aug 12, 2026, the average hourly pay for fraud detection machine learning in Philadelphia, PA is $18.22, according to ZipRecruiter salary data. Most workers in this role earn between $15.05 and $19.42 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals working in fraud detection machine learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What are the key skills and qualifications needed to thrive as a fraud detection machine learning specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.
What are popular job titles related to Fraud Detection Machine Learning jobs in Philadelphia, PA? For Fraud Detection Machine Learning jobs in Philadelphia, PA, the most frequently searched job titles are:
What job categories do people searching Fraud Detection Machine Learning jobs in Philadelphia, PA look for? The top searched job categories for Fraud Detection Machine Learning jobs in Philadelphia, PA are:
What cities near Philadelphia, PA are hiring for Fraud Detection Machine Learning jobs? Cities near Philadelphia, PA with the most Fraud Detection Machine Learning job openings:
Fraud Detection Machine Learning jobs near you

CCB Risk Program Associate

JPMorgan Chase & Co.

Wilmington, DE • On-site

$135K - $165K/yr

Full-time

Medical, Retirement

Posted 26 days ago

JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 493 frontline employees who took The Breakroom Quiz

72nd of 171 rated banks

Job description


The CCB Risk Modeling team is seeking talented professionals with expertise in computer vision, Generative AI, and image generation, with a focus on fraud modeling applications. Our work centers on building vision and multimodal AI systems-including image understanding, visual anomaly detection, synthetic data/image generation, and GenAI-enabled workflows-across modern ML platforms and emerging agentic patterns. The ideal candidate will drive these initiatives across model development, evaluation and monitoring tooling, and cross-functional collaboration, ensuring AI/ML solutions are robust, scalable, and aligned with governance, risk, and regulatory expectations.
Key Responsibilities
  1. Model Development: Design and develop machine learning models to drive impactful fraud modeling, covering the entire customer lifecycle, including acquisition, account management, transaction authorization, and collections.
  2. Advanced Machine Learning Techniques: Apply state-of-the-art machine learning methodologies - including deep learning architecture, transformer-based models, and LLMs - on big data platforms to tackle complex business challenges.
  3. Strategic Collaboration: Work closely with senior management to develop and implement ambitious, innovative modeling solutions, ensuring their successful deployment into production environments.
  4. Cross-Functional Partnership: Collaborate with diverse teams, including risk, technology, model governance, and research, throughout the entire modeling lifecycle-from development and review to deployment and operational use.

Basic Qualifications
  1. Ph.D. or Master's degree from a reputable institution in a quantitative discipline such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
  2. 5+ years' experience in creating predictive models, and generative AI solutions using LLM prompt engineering.
  3. Hands-on experience with LLM APIs, Python libraries like Pandas, NumPy, scikit-learn, and others for data manipulation, modeling and analysis.
  4. In-depth knowledge of advanced machine learning algorithms, including logistic regression, XGBoost, Deep Neural Networks (CNN and RNN), clustering, and recommendation systems, with expertise in model design, hyperparameter tuning, and responsible deployment practices.
  5. Demonstrated experience in model interpretability and explainability for complex models such as XGBoost and GBM; experience extending these methods to deep learning architectures (CNNs, RNNs, transformers) is a strong plus.
  6. Familiarity with large language models (LLMs) and their applications, including experience in fine-tuning, prompt engineering, and responsible deployment with appropriate safeguards, monitoring, and auditability.
  7. Proficiency in Python, TensorFlow, PyTorch, Spark, or Scala, coupled with experience in big data technologies such as Hadoop, AWS, and Hive, and familiarity with MLOps tooling that supports model monitoring, drift detection, and end-to-end auditability.

Preferred Qualifications
  1. Strong expertise, interest, and track record of performing cutting-edge research on Gen-AI
  2. Proven track record in designing, building, and deploying high-quality machine learning models in production environments, demonstrating a strong ability to translate theoretical concepts into practical applications.
  3. Demonstrated expertise in data wrangling and model building on a distributed Cloud computation environment (with stability, scalability and efficiency). GPU experience is desired.
  4. Strong ownership and execution; proven experience in implementing models in production.

About Us
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
About the Team
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.
We are here to help you manage your money with checking, savings and credit cards, combining the latest banking technology with comprehensive solutions to meet the financial needs of nearly half of U.S. households.

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